R Notebook
Differential analysis
Protein
Read in all the DPE files calculated by JF Trempe Lab or/and TMT-analyst by RL Files separated by genotype/iPSC line in the workbook “ProcessFilesRenameAccession.Rmd” All data is from 6 weeks DANs from iPSC lines in AIW002-02 background Bright genome and dark genome where run separately
# read in csv into to make a list of dataframes
# Load required library
library(readr)
# the protein DE files are here
folder_path <- "/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/DPE_files/"
# List all CSV files in the folder
csv_files <- list.files(path = folder_path, pattern = "\\.csv$", full.names = TRUE)
# Read all CSV files into a list of dataframes, skipping the first column
df_list <- lapply(csv_files, function(file) {
read_csv(file, col_types = cols(.default = "?", `...1` = col_skip()))
})New names:
• `` -> `...1`
New names:
• `` -> `...1`
New names:
• `` -> `...1`
New names:
• `` -> `...1`
New names:
• `` -> `...1`
New names:
• `` -> `...1`
New names:
• `` -> `...1`
New names:
• `` -> `...1`# Optionally, name each element of the list with the respective file names (without the .csv extension)
names(df_list) <- tools::file_path_sans_ext(basename(csv_files))
# Print the names of the dataframes
print(names(df_list))[1] "GBA-KO_ProtomicsDifferentialAbundance" "IGSF9B-KO_ProtomicsDifferentialAbundance" "INPP5F-KO_ProtomicsDifferentialAbundance"
[4] "IP6K2-KO_ProtomicsDifferentialAbundance" "PINK1-KO_ProtomicsDifferentialAbundance" "PRKN-KO_ProtomicsDifferentialAbundance"
[7] "SH3GL2-KO_ProtomicsDifferentialAbundance" "SNCA-A53T_ProtomicsDifferentialAbundance"# test that these are dataframes
df.gba <- df_list$`GBA-KO_ProtomicsDifferentialAbundance`
head(df.gba)NA
NARename the list
print(names(df_list))
[1] "GBA-KO" "IGSF9B-KO" "INPP5F-KO" "IP6K2-KO" "PINK1-KO" "PRKN-KO" "SH3GL2-KO" "SNCA-A53T"head(df_list$`GBA-KO`)Volcano plots
Thresholds: Log2 abundance ratio > 0.5 and p value < 0.05
pSH3GL2 <- EnhancedVolcano(df_list$`SH3GL2-KO`,
lab = df_list$`SH3GL2-KO`$Symbol,
x = 'log2_ratio',
y = 'p-value',
pCutoff = 0.05,
FCcutoff = 0.5,
colAlpha = 0.5,
labSize = 5,
xlim = c(-3,2.5),
ylim = c(0, 10.5),
drawConnectors = FALSE,
widthConnectors = 0.1,
max.overlaps = 40,
legendPosition = "right",
title = "SH3GL2-KO vs Control",
subtitle = "Differential Protein Abundance"
) + scale_x_continuous(breaks = seq(-3, 2.5, by = 0.5)) + # Adjust the x-axis breaks as needed
scale_y_continuous(limits = c(0, 10.5), expand = c(0, 0)) + # Remove space below the y-axis
coord_cartesian(xlim = c(-3, 2.5), ylim = c(0, 10.5)) + # Ensure that the plot does not display points beyond this range
theme(axis.text.x = element_text(size = 14), axis.text.y = element_text(size = 14)) # Adjust x-axis label size
Scale for x is already present.
Adding another scale for x, which will replace the existing scale.
Scale for y is already present.
Adding another scale for y, which will replace the existing scale.
Coordinate system already present. Adding new coordinate system, which will replace the existing one.pA53T
pGBA
pPINK1
pPRKN
pIGSF9B
pINPP5F
pIP6K2
pSH3GL2
Make a filtered list of dataframes with the thresholds
colnames(df_list$`GBA-KO`)[1] "Accession" "Symbol" "Description" "log2_ratio" "p-value" head(df_list$`IGSF9B-KO`)
head(df_list_numeric$`IGSF9B-KO`)
filter_dge_lists <- function(dge_lists, logFC_threshold = 0.25, logFC_direction = "both", p_threshold = 0.01, p_col = "p-value") {
# Iterate over each dataframe in the list
dge_lists_filtered <- lapply(dge_lists, function(dge_df) {
# Debugging: Print column names of the dataframe being processed
print(paste("Processing dataframe with columns:", paste(colnames(dge_df), collapse = ", ")))
# Apply the filter function to each dataframe
filtered_df <- filter_dge_results(dge_df, logFC_threshold, logFC_direction, p_threshold, p_col)
return(filtered_df)
})
return(dge_lists_filtered)
}
# Example usage
filtered_DEP <- filter_dge_lists(df_list, logFC_threshold = 0.5, logFC_direction = "both", p_threshold = 0.05, p_col = "p-value")
[1] "Processing dataframe with columns: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Columns in dataframe: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Classes of columns: character, character, character, numeric, numeric"
[1] "Processing dataframe with columns: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Columns in dataframe: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Classes of columns: character, character, character, numeric, numeric"
[1] "Processing dataframe with columns: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Columns in dataframe: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Classes of columns: character, character, character, numeric, numeric"
[1] "Processing dataframe with columns: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Columns in dataframe: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Classes of columns: character, character, character, numeric, numeric"
[1] "Processing dataframe with columns: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Columns in dataframe: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Classes of columns: character, character, character, numeric, numeric"
[1] "Processing dataframe with columns: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Columns in dataframe: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Classes of columns: character, character, character, numeric, numeric"
[1] "Processing dataframe with columns: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Columns in dataframe: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Classes of columns: character, character, character, numeric, numeric"
[1] "Processing dataframe with columns: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Columns in dataframe: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Classes of columns: character, character, character, numeric, numeric"Get gene counts
# function to count up and down regulated Proteins
count_regulations <- function(dge_df) {
# Ensure columns are numeric
dge_df$log2_ratio <- as.numeric(dge_df$log2_ratio)
# Count upregulated and downregulated proteins
upregulated_count <- sum(dge_df$log2_ratio > 0, na.rm = TRUE)
downregulated_count <- sum(dge_df$log2_ratio < 0, na.rm = TRUE)
return(c(Upregulated = upregulated_count, Downregulated = downregulated_count))
}
summarize_regulations <- function(dge_lists) {
# Get names of the dataframes
df_names <- names(dge_lists)
# Apply the counting function to each dataframe and name the result
counts_list <- lapply(dge_lists, function(df) {
counts <- count_regulations(df)
return(counts)
})
# Convert the list of counts into a dataframe
result_df <- do.call(rbind, counts_list)
# Set the row names to the names of the original dataframes
rownames(result_df) <- df_names
return(result_df)
}
# apply to filtered list
regulation_summary <- summarize_regulations(filtered_DEP)
print(regulation_summary) Upregulated Downregulated
GBA-KO 109 137
IGSF9B-KO 559 738
INPP5F-KO 144 209
IP6K2-KO 59 87
PINK1-KO 274 496
PRKN-KO 373 573
SH3GL2-KO 133 116
SNCA-A53T 162 244colnames(filtered_DEP$`PINK1-KO`)[1] "Accession" "Symbol" "Description" "log2_ratio" "p-value" Function to get the top n genes up and down
# Function to select top n up and down regulated genes
select_top_genes <- function(dge_df, logFC_col = "log2_ratio", symbol_col = "Symbol", n = 10) {
# Ensure log2_ratio column is numeric
dge_df[[logFC_col]] <- as.numeric(dge_df[[logFC_col]])
# Sort dataframe by log2_ratio to get top upregulated and downregulated genes
top_upregulated <- dge_df %>%
arrange(desc(!!sym(logFC_col))) %>%
head(n) %>%
pull(!!sym(symbol_col))
top_downregulated <- dge_df %>%
arrange(!!sym(logFC_col)) %>%
head(n) %>%
pull(!!sym(symbol_col))
# Combine upregulated and downregulated genes into a single vector
top_genes <- c(top_upregulated, top_downregulated)
return(top_genes)
}
# Example usage
top_genes <- select_top_genes(filtered_DEP$`PINK1-KO`, n = 10)
print(top_genes) [1] "NEFL" "PLXNA4" "VSNL1" "NEFM" "PALM3" "STX1A" "ATP5PF" "SLC25A13" "SYT2" "DIRAS2" "CA2"
[12] "SNRPA" "PROCR" "HDGF" "A0A087WY61" "ZNF207" "CD99" "HMGN3" "DCN" "LMNA" top_genes <- select_top_genes(filtered_DEP$`PINK1-KO`, n = 5)
print(top_genes)
[1] "NEFL" "PLXNA4" "VSNL1" "NEFM" "PALM3" "CA2" "SNRPA" "PROCR" "HDGF" "A0A087WY61"
df.pink1 <- filtered_DEP$`PINK1-KO`
Plot a heatmap of the top up and down genes
heatmap_plot <- plot_protein_heatmap(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO")
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"# Print the plot
print(heatmap_plot)
Plot control and IPSC line for each list
# Example usage
# Assuming 'df' is your dataframe with relative abundance data
heatmap_plot <- plot_protein_heatmap(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO")
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"# Print the plot
print(heatmap_plot)
NA
NAz-score
#library(ggplot2)
#library(dplyr)
#library(tidyr)
#data = df
#proteins = top_genes
#sample_patterns = c("Control", "PINK1.KO")
# Function to plot heatmap of relative abundance with Z-scores
plot_protein_heatmap_zscore <- function(data, proteins, sample_patterns, na_color = "grey"){
# Filter the data for selected proteins
data_filtered <- data %>%
filter(Symbol %in% proteins)
# Set the order of the Symbol factor based on the input vector 'proteins'
data_filtered$Symbol <- factor(data_filtered$Symbol, levels = proteins)
# Identify sample columns matching patterns
sample_columns <- colnames(data_filtered)[sapply(colnames(data_filtered), function(col_name) {
any(sapply(sample_patterns, function(p) grepl(p, col_name)))
})]
# Debug: Check contents of sample_columns
print(paste("Sample columns selected:", paste(sample_columns, collapse = ", ")))
# Check if sample_columns has valid entries
if (length(sample_columns) == 0) {
stop("No sample columns matched the patterns provided.")
}
# Select only columns matching sample patterns and the Symbol column
data_filtered <- data_filtered %>%
dplyr::select(Symbol, all_of(sample_columns))
# Remove rows where all values are NA (excluding the Symbol column)
data_filtered <- data_filtered %>%
filter(rowSums(is.na(dplyr::select(., -Symbol))) < length(sample_columns))
# Calculate Z-scores for each protein across the selected samples
data_zscore <- data_filtered %>%
mutate(across(all_of(sample_columns), ~ scale(.)[, 1], .names = "z_{col}"))
# Reshape data for ggplot
data_long <- data_zscore %>%
pivot_longer(
cols = starts_with("z_"),
names_to = "Sample",
values_to = "Abundance"
) %>%
mutate(Sample = gsub("z_", "", Sample)) # Remove 'z_' prefix for clean sample names
# Create the heatmap
heatmap_plot <- ggplot(data_long, aes(x = Sample, y = Symbol, fill = Abundance)) +
geom_tile(color = "white") +
scale_fill_gradientn(
#colors = c("#ffffff", "#ffcccc", "#ff6666", "#ff3333","#fa0505", "#cc0000","#990000"),
#colors = c("#fdfef4", "#DAF7A6", "#FFC300", "#FF5733","#e71f05","#4d0b02"),
colors = c("snow","lightgoldenrod1","gold1","darkorange1","red2","firebrick4"),
values = scales::rescale(c(-0.5, -0.25, 0, 1,2,2.5,2.75)),
na.value = na_color,
guide = guide_colorbar(
barwidth = 1,
barheight = 10,
title.position = "top",
title.hjust = 0.5
)) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Protein Abundance Heatmap (Z-Score)", x = "Samples", y = "Proteins")
return(heatmap_plot)
}
# Example usage
heatmap_plot <- plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO")
)[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"# Print the plot
print(heatmap_plot)
NA
NA
NA
NAAdjust the function
library(ggplot2)
library(dplyr)
library(tidyr)
# Function to plot heatmap of relative abundance with Z-scores
plot_protein_heatmap_zscore <- function(data, proteins, sample_patterns, colors, scale_values, na_color = "grey") {
# Filter the data for selected proteins
data_filtered <- data %>%
filter(Symbol %in% proteins)
# Set the order of the Symbol factor based on the input vector 'proteins'
data_filtered$Symbol <- factor(data_filtered$Symbol, levels = proteins)
# Identify sample columns matching patterns
sample_columns <- colnames(data_filtered)[sapply(colnames(data_filtered), function(col_name) {
any(sapply(sample_patterns, function(p) grepl(p, col_name)))
})]
# Debug: Check contents of sample_columns
print(paste("Sample columns selected:", paste(sample_columns, collapse = ", ")))
# Check if sample_columns has valid entries
if (length(sample_columns) == 0) {
stop("No sample columns matched the patterns provided.")
}
# Select only columns matching sample patterns and the Symbol column
data_filtered <- data_filtered %>%
dplyr::select(Symbol, all_of(sample_columns))
# Remove rows where all values are NA (excluding the Symbol column)
data_filtered <- data_filtered %>%
filter(rowSums(is.na(dplyr::select(., -Symbol))) < length(sample_columns))
# Calculate Z-scores for each protein across the selected samples
data_zscore <- data_filtered %>%
mutate(across(all_of(sample_columns), ~ scale(.)[, 1], .names = "z_{col}"))
# Reshape data for ggplot
data_long <- data_zscore %>%
pivot_longer(
cols = starts_with("z_"),
names_to = "Sample",
values_to = "Abundance"
) %>%
mutate(Sample = gsub("z_", "", Sample)) # Remove 'z_' prefix for clean sample names
# Create the heatmap
heatmap_plot <- ggplot(data_long, aes(x = Sample, y = Symbol, fill = Abundance)) +
geom_tile(color = "white") +
scale_fill_gradientn(
colors = colors,
values = scales::rescale(scale_values),
na.value = na_color,
guide = guide_colorbar(
barwidth = 1,
barheight = 10,
title.position = "top",
title.hjust = 0.5
)
) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Protein Abundance Heatmap (Z-Score)", x = "Samples", y = "Proteins")
return(heatmap_plot)
}
# Example usage
heatmap_plot <- plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-2.5, -2, -1, 0, 1, 2, 2.5) # Adjust based on your data range
)[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"# Print the plot
print(heatmap_plot)
NA
NA
# Example usage
heatmap_plot <- plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-2.5, -2, -1, 0, 1, 2, 2.5), # Adjust based on your data range
group_means = TRUE # Set to FALSE if you want individual samples
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"# Print the plot
print(heatmap_plot)
# Example usage
heatmap_plot <- plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-2.5, -2, -1, 0, 1, 2, 2.5), # Adjust based on your data range
group_means = FALSE # Set to FALSE if you want individual samples
)[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"# Print the plot
print(heatmap_plot)
Control width
library(ggplot2)
library(dplyr)
library(tidyr)
# Function to plot heatmap of relative abundance with Z-scores
plot_protein_heatmap_zscore <- function(data, proteins, sample_patterns, colors, scale_values, na_color = "grey", group_means = FALSE, tile_width = 0.9) {
# Filter the data for selected proteins
data_filtered <- data %>%
filter(Symbol %in% proteins)
# Set the order of the Symbol factor based on the input vector 'proteins'
data_filtered$Symbol <- factor(data_filtered$Symbol, levels = proteins)
# Identify sample columns matching patterns
sample_columns <- colnames(data_filtered)[sapply(colnames(data_filtered), function(col_name) {
any(sapply(sample_patterns, function(p) grepl(p, col_name)))
})]
# Debug: Check contents of sample_columns
print(paste("Sample columns selected:", paste(sample_columns, collapse = ", ")))
# Check if sample_columns has valid entries
if (length(sample_columns) == 0) {
stop("No sample columns matched the patterns provided.")
}
# Select only columns matching sample patterns and the Symbol column
data_filtered <- data_filtered %>%
dplyr::select(Symbol, all_of(sample_columns))
# Remove rows where all values are NA (excluding the Symbol column)
data_filtered <- data_filtered %>%
filter(rowSums(is.na(dplyr::select(., -Symbol))) < length(sample_columns))
if (group_means) {
# Group samples by the base name and calculate mean
sample_base <- gsub("\\.\\d+$", "", colnames(data_filtered)[-1])
data_grouped <- data_filtered %>%
pivot_longer(cols = -Symbol, names_to = "Sample", values_to = "Abundance") %>%
mutate(SampleBase = gsub("\\.\\d+$", "", Sample)) %>%
group_by(Symbol, SampleBase) %>%
summarize(Abundance = mean(Abundance, na.rm = TRUE), .groups = 'drop') %>%
pivot_wider(names_from = SampleBase, values_from = Abundance)
# Calculate Z-scores
data_zscore <- data_grouped %>%
mutate(across(-Symbol, ~ scale(.)[, 1], .names = "z_{col}"))
# Reshape data for ggplot
data_long <- data_zscore %>%
pivot_longer(
cols = starts_with("z_"),
names_to = "Sample",
values_to = "Abundance"
) %>%
mutate(Sample = gsub("z_", "", Sample)) # Remove 'z_' prefix for clean sample names
} else {
# Calculate Z-scores for each protein across the selected samples
data_zscore <- data_filtered %>%
mutate(across(all_of(sample_columns), ~ scale(.)[, 1], .names = "z_{col}"))
# Reshape data for ggplot
data_long <- data_zscore %>%
pivot_longer(
cols = starts_with("z_"),
names_to = "Sample",
values_to = "Abundance"
) %>%
mutate(Sample = gsub("z_", "", Sample)) # Remove 'z_' prefix for clean sample names
}
# Create the heatmap
heatmap_plot <- ggplot(data_long, aes(x = Sample, y = Symbol, fill = Abundance)) +
geom_tile(color = "white") +
scale_fill_gradientn(
colors = colors,
values = scales::rescale(scale_values),
na.value = na_color,
guide = guide_colorbar(
barwidth = 1,
barheight = 10,
title.position = "top",
title.hjust = 0.5
)
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
aspect.ratio = 1 / tile_width # Adjust aspect ratio
) +
labs(title = "Protein Abundance Heatmap (Z-Score)", x = "Samples", y = "Proteins")
return(heatmap_plot)
}
# Example usage
heatmap_plot <- plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-2.5, -2, -1, 0, 1, 2, 2.5), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
tile_width = 0.25 # Adjust the width of the tiles (default is 0.9)
)[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"# Print the plot
print(heatmap_plot)
NA
NAFunction to see the gene expression
max(df.long$Abundance)
[1] 3.575772min(df.long$Abundance)[1] -0.490151Check each contrast
top_genes <- select_top_genes(filtered_DEP$`PINK1-KO`, n = 10)
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-0.5, -0.25, 0, 1, 2, 4), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
tile_width = 0.25
)[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-0.5, -0.25, 0, 1, 2, 4), # Adjust based on your data range
group_means = FALSE,# Set to FALSE if you want individual samples
tile_width = 0.25
)[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-0.9,-0.6,-0.2, 0, 2, 2.5), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-0.9,-0.6,-0.2, 0, 2, 2.5), # Adjust based on your data range
group_means = FALSE,# Set to FALSE if you want individual samples
tile_width = 0.25
)[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"
colnames(df) [1] "Symbol" "A53T.1" "A53T.2" "A53T.3" "Control.1" "Control.2" "Control.3" "GBA.KO.1" "GBA.KO.2" "GBA.KO.3" "PINK1.KO.1"
[12] "PINK1.KO.2" "PINK1.KO.3" "PRKN.KO.1" "PRKN.KO.2" "PRKN.KO.3" SNCA-A53T
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "A53T"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1, -0.5,-0.25, 0, 1, 2, 4), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: A53T.1, A53T.2, A53T.3, Control.1, Control.2, Control.3"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "A53T"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1, -0.5, -0.25,0, 1, 2, 4), # Adjust based on your data range
group_means = FALSE,# Set to FALSE if you want individual samples
tile_width = 0.25
)[1] "Sample columns selected: A53T.1, A53T.2, A53T.3, Control.1, Control.2, Control.3"
GBA
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "GBA.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1, -0.5, -0.25,0, 1, 2, 3.5), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, GBA.KO.1, GBA.KO.2, GBA.KO.3"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "GBA.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1, -0.5, -0.25,0, 1, 2, 3.5), # Adjust based on your data range
group_means = FALSE,# Set to FALSE if you want individual samples
tile_width = 0.25
)[1] "Sample columns selected: Control.1, Control.2, Control.3, GBA.KO.1, GBA.KO.2, GBA.KO.3"
Parkin KO
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PRKN.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1, -0.5, -0.25,0, 1, 2, 3.5), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, PRKN.KO.1, PRKN.KO.2, PRKN.KO.3"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PRKN.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1, -0.5, -0.25,0, 1, 2, 3.5), # Adjust based on your data range
group_means = FALSE,# Set to FALSE if you want individual samples
tile_width = 0.25
)[1] "Sample columns selected: Control.1, Control.2, Control.3, PRKN.KO.1, PRKN.KO.2, PRKN.KO.3"
For the dark genome gene expression levels I’ll need the other dataframe
colnames(df)
[1] "Symbol" "Control.1" "Control.2" "Control.3" "Control.4" "IGSF9B.KO.1" "IGSF9B.KO.2" "INPP5F.KO.1" "INPP5F.KO.2" "INPP5F.KO.3"
[11] "IP6K2.KO.1" "IP6K2.KO.2" "IP6K2.KO.4" "SH3GL2.KO.1" "SH3GL2.KO.2" "SH3GL2.KO.3"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "IGSF9B.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1, -0.5, -0.25,0, 1, 2, 3.5), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IGSF9B.KO.1, IGSF9B.KO.2"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "IGSF9B.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1, -0.5, -0.25,0, 1, 2, 3.5), # Adjust based on your data range
group_means = FALSE,# Set to FALSE if you want individual samples
tile_width = 0.25
)[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IGSF9B.KO.1, IGSF9B.KO.2"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "INPP5F.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
scale_values = c(-0.75, -0.5, -0.25,0, 1, 2, 4), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, INPP5F.KO.1, INPP5F.KO.2, INPP5F.KO.3"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "INPP5F.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
scale_values = c(-0.75, -0.5, -0.25,0, 1, 2, 4), # Adjust based on your data range
group_means = FALSE,# Set to FALSE if you want individual samples
tile_width = 0.25
)[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, INPP5F.KO.1, INPP5F.KO.2, INPP5F.KO.3"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "IP6K2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
scale_values = c(-0.75, -0.5, -0.25,0, 1, 2, 4), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IP6K2.KO.1, IP6K2.KO.2, IP6K2.KO.4"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "IP6K2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
scale_values = c(-0.75, -0.5, -0.25,0, 1, 2, 4), # Adjust based on your data range
group_means = FALSE,# Set to FALSE if you want individual samples
tile_width = 0.25
)[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IP6K2.KO.1, IP6K2.KO.2, IP6K2.KO.4"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "SH3GL2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
scale_values = c(-0.6, -0.5,0.25, 0, 1, 3.8, 4.1), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "SH3GL2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
scale_values = c(-0.6, -0.5,0.25, 0, 1, 3.8, 4.1), # Adjust based on your data range
group_means = FALSE,# Set to FALSE if you want individual samples
tile_width = 0.25
)[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
names(filtered_DEP)
[1] "GBA-KO" "IGSF9B-KO" "INPP5F-KO" "IP6K2-KO" "PINK1-KO" "PRKN-KO" "SH3GL2-KO" "SNCA-A53T"gene overlap
# Create the UpSet plot
upset(
gene_matrix,
sets = names(gene_lists),
sets.bar.color = "#56B4E9",
order.by = "freq",
empty.intersections = "on",
keep.order = TRUE
)
Control the order
p <- create_upset_plot(filtered_DEP, contrast_order, colors = rev(colors), text_scale = 1.5)
'data.frame': 2314 obs. of 9 variables:
$ Symbol : chr "HDLBP" "EHD1" "OSTC" "VTA1" ...
$ IP6K2-KO : num 0 0 0 0 0 0 0 0 0 0 ...
$ SH3GL2-KO: num 0 0 0 0 0 0 0 0 0 0 ...
$ INPP5F-KO: num 0 0 0 0 0 0 0 0 0 0 ...
$ IGSF9B-KO: num 0 0 1 1 1 0 1 0 0 1 ...
$ PRKN-KO : num 1 1 0 1 0 0 1 0 1 1 ...
$ PINK1-KO : num 1 1 1 1 0 0 1 0 1 1 ...
$ GBA-KO : num 1 1 1 1 1 1 1 1 1 1 ...
$ SNCA-A53T: num 1 0 1 0 0 0 1 1 1 1 ...
NULLprint(p)
pdf("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/UpsetplotBrightandDark.pdf", width = 10, height = 5.5)
p
dev.off()
null device
1 See which genes overlap - function
# bright genome overlap
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO","PRKN-KO") # Specify the contrasts of interest
result.bright <- get_gene_overlap_and_unique(filtered_DEP, contrast_list)
# Print results
print(result.bright$`Overlapping Genes`) # Genes common across all specified contrasts
[1] "HDLBP" "NCAM1" "A0A0J9YYL3" "STXBP1" "A0A1P0AYU5" "RPS14" "PALM3" "HSPA12A" "PRKRA" "P4HB" "OBSCN"
[12] "SNRPE" "SCFD1" "CYCS" "CTSD" "RPS3A" "SNCA" "NEFM" "TP53I11" "F8W6I7" "CD44" "H3BQZ7"
[23] "CBX1" "SLC16A3" "HNRNPUL1" "C11orf58" "VGF" "DCX" "KPNA6" "CLN5" "SNCG" "LY6H" "HRAS"
[34] "FUCA1" "ANXA1" "ATP1B1" "SSB" "KRT18" "PYGL" "NEFL" "ANXA2" "DCN" "SYP" "ANXA5"
[45] "GSTP1" "GNAO1" "HSPA5" "RALA" "ATP1A3" "P4HA1" "GNS" "PPP3CB" "SYN1" "GAP43" "NCL"
[56] "FLNA" "MAOA" "HNRNPA2B1" "ATP2B4" "FKBP2" "EPHB2" "EEF1D" "CRABP1" "PRDX6" "LRPAP1" "RPS19"
[67] "PRPH" "ACAA2" "SRP9" "SERPINH1" "HDGF" "RPS8" "RPS23" "RPS13" "RPL23A" "GNB1" "RPL10A"
[78] "ABAT" "ERH" "SET" "PLOD1" "NUCB1" "CRYZ" "LGALS3BP" "AHNAK" "GALNT2" "CNTN1" "DNAJC3"
[89] "TRIM28" "ALCAM" "STX1A" "FNDC3B" "GDAP1L1" "CEP68" "CEND1" "CADM2" "GLG1" "CMBL" "DIRAS2"
[100] "FUCA2" "GOLPH3" "CTPS2" "TMOD3" "NTM" "RRBP1" "PLXNA1" "SEC23IP" #print(result$`Unique Genes`) # List of genes unique to each contrast
Function isn’t exactly correct need to fix
intersect(pink.df$Accession, prkn.df$Accession)
[1] "A0A024R4E5" "A0A024R571" "A0A087WTT1" "A0A087WWD4" "A0A087WWU8" "A0A087WY55" "A0A0A0MQX8" "A0A0A6YYA0" "A0A0D9SF30" "A0A0D9SF98" "A0A0J9YXF2"
[12] "A0A0J9YYL3" "A0A1B0GTP9" "A0A1P0AYU5" "A0A1W2PQR6" "A0A2R8Y5S7" "A0A2R8Y6W5" "A0A2R8Y811" "A0A2R8Y849" "A0A2R8YDT1" "A0A3B3IU69" "A0A494BWY4"
[23] "A0A494C1E2" "A0A590UJ23" "A0A5F9UP49" "A0A5F9ZHL1" "A0A669KB89" "A0A6I8PIW1" "A0A6Q8PFE5" "A0A6Q8PFJ0" "A0A6Q8PGS2" "A0A7I2V3I2" "A0A7I2V4B3"
[34] "A0A7I2V4E4" "A0A7I2V535" "A0A7I2V5H3" "A0A7I2YQT2" "A0A7P0PJI2" "A0A7P0TA35" "A0A7P0TA76" "A0A7P0TAG7" "A0A7P0TAQ9" "A0A804HK65" "A0A804HL40"
[45] "A2A2V1" "A6NFX8" "A6NGQ3" "A6NHK2" "A6NNK5" "A8MU27" "A8MXP9" "B0QZK4" "B1ALY0" "B3KS98" "B4DHE8"
[56] "B4DLN1" "B4DLR8" "B5ME19" "B7Z5N7" "B7ZC39" "C9J3D7" "C9J813" "C9JFR7" "C9JH19" "C9JZR2" "D6RAT0"
[67] "D6REX3" "D6RH20" "E2QRB3" "E7EPV7" "E7ESP9" "E7EUC7" "E7EX17" "E7EX73" "E9PC15" "E9PDF2" "E9PF59"
[78] "E9PFH4" "E9PHY5" "E9PIN5" "E9PJP2" "E9PNF7" "F5GWR7" "F5GYQ1" "F5GZS6" "F8VW96" "F8VZX2" "F8W6I7"
[89] "G3V0I5" "G3V186" "G3V1L9" "G3V3E8" "H0Y3P2" "H0Y938" "H0YD13" "H0YHX9" "H0YKD8" "H3BN98" "H3BQZ7"
[100] "H3BQZ9" "H3BUF6" "H7BZJ3" "H7C1N3" "H7C2N1" "H9KV31" "J3KNP2" "J3KS05" "J3KS31" "J3QQV2" "K7ELL7"
[111] "K7ENE8" "K7ERC8" "M0R0F0" "M0R181" "M0R210" "M0R3F1" "O00193" "O00425" "O00461" "O00625" "O00754"
[122] "O15240" "O43143" "O43175" "O43181" "O43242" "O43399" "O43602" "O43615" "O43639" "O43684" "O60313"
[133] "O60684" "O60763" "O60831" "O60841" "O75306" "O75368" "O75369" "O75390" "O75475" "O75489" "O75503"
[144] "O75525" "O75718" "O75746" "O76003" "O76070" "O94772" "O94826" "O94925" "O95197" "O95302" "O95336"
[155] "O95631" "P00390" "P00403" "P00505" "P00738" "P01112" "P02786" "P04066" "P04080" "P04083" "P04181"
[166] "P04843" "P05026" "P05204" "P05413" "P05455" "P05783" "P05787" "P05937" "P06737" "P06756" "P07196"
[177] "P07355" "P07585" "P07858" "P07954" "P08247" "P08670" "P08758" "P08865" "P09211" "P09471" "P09874"
[188] "P09960" "P09972" "P10155" "P10253" "P10909" "P11021" "P11233" "P11279" "P11413" "P12004" "P12036"
[199] "P12532" "P13010" "P13637" "P13667" "P13674" "P14406" "P15289" "P15586" "P15880" "P16152" "P16278"
[210] "P16298" "P16615" "P16671" "P17050" "P17600" "P17677" "P18077" "P18859" "P19338" "P19367" "P20020"
[221] "P20073" "P20962" "P21283" "P21333" "P21397" "P21796" "P21912" "P22307" "P22626" "P23396" "P23526"
[232] "P23634" "P24539" "P25398" "P25705" "P25788" "P26012" "P26232" "P26373" "P26641" "P26885" "P26992"
[243] "P27635" "P27797" "P29323" "P29401" "P29692" "P29762" "P30041" "P30044" "P30533" "P30536" "P30837"
[254] "P31689" "P32004" "P32189" "P32969" "P35221" "P35240" "P36542" "P37108" "P37837" "P38435" "P39019"
[265] "P39023" "P40261" "P40926" "P41219" "P42765" "P43121" "P43155" "P45973" "P46783" "P46940" "P46977"
[276] "P48444" "P48681" "P49189" "P49458" "P49755" "P49915" "P50148" "P50454" "P50995" "P51571" "P51572"
[287] "P51798" "P51858" "P52306" "P52655" "P52788" "P52815" "P53396" "P53618" "P53621" "P54709" "P54727"
[298] "P54802" "P55036" "P55809" "P58876" "P59768" "P60033" "P60174" "P60842" "P60866" "P60880" "P61224"
[309] "P61764" "P62081" "P62241" "P62266" "P62269" "P62277" "P62701" "P62750" "P62760" "P62873" "P62888"
[320] "P62906" "P63096" "P63220" "P63244" "P67936" "P68104" "P78357" "P78527" "P80303" "P80404" "P80723"
[331] "P83731" "P84090" "Q00577" "Q00688" "Q01105" "Q01581" "Q02809" "Q02818" "Q06323" "Q08209" "Q08211"
[342] "Q08257" "Q08380" "Q09666" "Q10471" "Q12841" "Q12860" "Q12907" "Q13217" "Q13263" "Q13310" "Q13442"
[353] "Q13740" "Q14152" "Q14315" "Q15019" "Q15102" "Q15149" "Q15365" "Q15369" "Q15424" "Q15555" "Q15651"
[364] "Q15768" "Q16186" "Q16352" "Q16531" "Q16623" "Q16778" "Q32P28" "Q4J6C6" "Q53EP0" "Q5JXI8" "Q5SQI0"
[375] "Q5SW79" "Q5SWX8" "Q5T1M5" "Q5T760" "Q5T7C4" "Q5TE61" "Q5URX0" "Q5ZPR3" "Q60FE5" "Q6DKJ4" "Q6P587"
[386] "Q6X4W1" "Q76N32" "Q7KZF4" "Q7L0Y3" "Q7Z4G1" "Q86TU7" "Q86UY8" "Q8N111" "Q8N3J6" "Q8N8L6" "Q8NBU5"
[397] "Q8NC51" "Q8TAT6" "Q8TB37" "Q8TCJ2" "Q8WVV9" "Q8WXD2" "Q92743" "Q92859" "Q92879" "Q92896" "Q92945"
[408] "Q969H8" "Q969X5" "Q96AQ6" "Q96AX1" "Q96AY3" "Q96CW9" "Q96DA6" "Q96DG6" "Q96E17" "Q96E39" "Q96FJ2"
[419] "Q96HU8" "Q96I24" "Q96N66" "Q96QR8" "Q96RD7" "Q96T51" "Q99460" "Q99471" "Q99733" "Q99747" "Q9BPW8"
[430] "Q9BRX8" "Q9BTY2" "Q9BUH6" "Q9BVM2" "Q9BW30" "Q9BYD2" "Q9BYT8" "Q9BZ95" "Q9H0A8" "Q9H115" "Q9H1E3"
[441] "Q9H1K0" "Q9H3N1" "Q9H492" "Q9H4A6" "Q9H845" "Q9H8Y8" "Q9H910" "Q9H936" "Q9H9J2" "Q9HAS0" "Q9HAV4"
[452] "Q9HAV7" "Q9HCJ6" "Q9NQ48" "Q9NQ66" "Q9NRF8" "Q9NRX4" "Q9NSE4" "Q9NVI7" "Q9NX76" "Q9NXG6" "Q9NY47"
[463] "Q9NYL9" "Q9NZ53" "Q9NZ72" "Q9NZI8" "Q9P016" "Q9P032" "Q9P0J0" "Q9P0V9" "Q9P121" "Q9P1W3" "Q9P2E9"
[474] "Q9UBT2" "Q9UEL6" "Q9UEY8" "Q9UHB9" "Q9UHL4" "Q9UII2" "Q9UIV1" "Q9UIW2" "Q9UJS0" "Q9UK22" "Q9UKY7"
[485] "Q9ULH1" "Q9UMX5" "Q9UNN8" "Q9UQ80" "Q9Y277" "Q9Y2B0" "Q9Y2J2" "Q9Y2X3" "Q9Y3D6" "Q9Y3I0" "Q9Y490"
[496] "Q9Y512" "Q9Y5B9" "Q9Y5L4" "Q9Y5P6" "Q9Y678" "Q9Y680" "Q9Y6M1" "Q9Y6Y8" # apply to filtered list
regulation_summary <- summarize_regulations(filtered_DEP)
print(regulation_summary) Upregulated Downregulated
GBA-KO 109 137
IGSF9B-KO 559 738
INPP5F-KO 144 209
IP6K2-KO 59 87
PINK1-KO 274 496
PRKN-KO 373 573
SH3GL2-KO 133 116
SNCA-A53T 162 244contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO","PRKN-KO","IGSF9B-KO","INPP5F-KO","IP6K2-KO","IGSF9B-KO", "INPP5F-KO", "IP6K2-KO") # Specify the contrasts of interest
all <- get_gene_overlap_and_unique(filtered_DEP, contrast_list)
print(all$`Overlapping Genes`)
names(filtered_DEP)[1] "GBA-KO" "IGSF9B-KO" "INPP5F-KO" "IP6K2-KO" "PINK1-KO" "PRKN-KO" "SH3GL2-KO" "SNCA-A53T"print(gba.pink1)
$`Overlapping Genes`
[1] "HDLBP" "EHD1" "OSTC" "VTA1" "NCAM1" "A0A0J9YYL3" "STXBP1" "A0A1P0AYU5" "HNRNPU" "RPL5" "RPS14"
[12] "SMARCE1" "A0A3B3IRQ9" "PALM3" "HSPA12A" "LMNA" "PRKRA" "P4HB" "OBSCN" "SNRPE" "EWSR1" "B4DLN1"
[23] "HNRNPC" "B4E171" "SCFD1" "CALD1" "CYCS" "CTSD" "RPS3A" "SNCA" "NEFM" "HNRNPH1" "TP53I11"
[34] "PRCP" "ATP6V0D1" "F8W6I7" "CD44" "H3BQZ7" "APRT" "ATXN2L" "HMGN1" "NCAM2" "RPL17" "CBX1"
[45] "DDX5" "SLC16A3" "SF3A2" "HNRNPUL1" "ACOT7" "C11orf58" "MAN2B1" "DPYSL4" "VGF" "SYT7" "DCX"
[56] "KPNA6" "CS" "PSIP1" "NDUFS3" "CLN5" "GLRX3" "SNCG" "CIAO1" "LY6H" "TOMM70" "DIRAS1"
[67] "HSPA4L" "TXNDC12" "GOT2" "CA2" "HRAS" "FUCA1" "ANXA1" "OAT" "ATP1B1" "HMGN2" "FABP3"
[78] "SSB" "KRT18" "PYGL" "NEFL" "ANXA2" "DCN" "FH" "ANXA6" "SYP" "VIM" "ANXA5"
[89] "GSTP1" "GNAO1" "HSPA5" "RALA" "XRCC6" "ATP1A3" "PDIA4" "P4HA1" "SNRPB" "HNRNPL" "EZR"
[100] "GNS" "PPP3CB" "NAGA" "SYN1" "GAP43" "ATP5PF" "NCL" "HK1" "LMNB1" "FLNA" "MAOA"
[111] "VDAC1" "HNRNPA2B1" "ATP2B4" "ATP5PB" "FKBP2" "CALR" "EPHB2" "EEF1D" "CRABP1" "PRDX6" "LRPAP1"
[122] "TSPO" "ALDH1B1" "GK" "RPS19" "PRPH" "ACAA2" "CBX5" "ALDH9A1" "FASN" "SRP9" "SERPINB9"
[133] "SERPINH1" "GPM6A" "HDGF" "STX1B" "RPS8" "RPS23" "RPS13" "RPL23A" "RPL23" "GNB1" "RPL10A"
[144] "ABAT" "ERH" "SET" "HMGCS1" "PLOD1" "NUCB1" "RPL6" "CRYZ" "LGALS3BP" "NCBP1" "AHNAK"
[155] "GALNT2" "FSTL1" "CNTN1" "DNAJC3" "TRIM28" "PDAP1" "ALCAM" "FLNC" "PLEC" "CNN3" "SNCB"
[166] "STX1A" "FNDC3B" "HMGB1" "GDAP1L1" "NXN" "FAHD1" "CEP68" "KTN1" "CEND1" "CADM2" "SYT2"
[177] "SERBP1" "PLBD2" "NUBPL" "CELF1" "GLG1" "ATP6V0A1" "CMBL" "RAB3C" "DIRAS2" "FUBP3" "RBM17"
[188] "SCN2A" "SEPTIN5" "NAPG" "DPYSL5" "FUCA2" "MAP1LC3A" "GOLPH3" "JPT2" "SLC25A22" "CTPS2" "PHPT1"
[199] "TMOD3" "SEPTIN10" "NTM" "RRBP1" "UBA2" "ADD3" "PLXNA1" "SLC25A13" "NENF" "THRAP3" "SAMM50"
[210] "SUPT16H" "SEC23IP"
$`Unique Genes`
$`Unique Genes`$`GBA-KO`
[1] "SNX12" "MADD" "A0A0G2JLB3" "C9JIZ6" "IDH3G" "SPARC" "NRXN2" "BAIAP2" "YES1" "PLXNC1" "SLC25A5"
[12] "SCG2" "ATP6V1B2" "COMT" "ITGA6" "MAOB" "SDHA" "RPL4" "PLCB3" "SELENBP1" "NNT" "ELAVL3"
[23] "QPRT" "RHEB" "MRPL23" "HSDL1" "RELL2" "ABCF1" "SMARCC2" "RAPGEF4" "RMDN1" "MAP7D2" "GDA"
$`Unique Genes`$`PINK1-KO`
[1] "A0A087WTM1" "PABPC1" "CLTC" "TPM3" "A0A087WY61" "TARDBP" "SRSF3" "CD99" "MYEF2"
[10] "MBNL1" "A0A0A0MR09" "NOLC1" "GSN" "ILK" "TMED7-TICAM2" "PON2" "ENAH" "QARS1"
[19] "SCARB2" "SEPTIN3" "RDX" "MEA1" "RPS24" "GLUL" "MOGS" "EML1" "TMEM132E"
[28] "PGM3" "DLG1" "AP3B2" "A0A5F9UP49" "ACAT1" "DDX17" "SCRIB" "TCOF1" "CAST"
[37] "GDAP1" "UBE3A" "TRIM2" "EIF3E" "ADAM9" "EIF4H" "NONO" "PPIG" "NPM1"
[46] "SYNCRIP" "PITRM1" "SRP54" "ACSL3" "SLC1A3" "DNAJC10" "RMND1" "HSD17B4" "RABGAP1L"
[55] "CRELD1" "LRPPRC" "IARS1" "SHTN1" "A2A2V1" "NUDT5" "PTBP1" "TP53BP1" "SUMO3"
[64] "MATR3" "HP1BP3" "PALM2AKAP2" "EIF3H" "MSI2" "NQO1" "ILF2" "PREB" "EIF3CL"
[73] "SH3GLB2" "CROT" "CTNND1" "PLRG1" "HNRNPAB" "SEC31A" "MRPS27" "CIRBP" "PYCR1"
[82] "SKP1" "SDC2" "UGP2" "EIF4B" "EIF4G1" "ALYREF" "AGK" "CELF2" "OGDH"
[91] "DPP6" "TNPO3" "ADCYAP1R1" "EPB41L2" "E9PJP2" "HSPA8" "UBTF" "RPL8" "PTPRD"
[100] "SLC3A2" "LAMTOR1" "CSRP2" "PCBP2" "UBAP2L" "F8WE88" "NDUFV1" "GRID1" "TJP1"
[109] "NPC2" "ARFGAP2" "H0Y3P2" "COPB2" "NACA" "RPL28" "H3BN98" "PDIA3" "BET1"
[118] "PTMA" "TBL3" "RPL36A" "TMEM199" "ZNF207" "SRSF1" "PRKCSH" "FXYD7" "FARSA"
[127] "KDSR" "GPX4" "RPS5" "RPL21" "RPS16" "RPL18A" "HIP1" "IGF2BP3" "GOLIM4"
[136] "HMGN4" "PIR" "LIN7A" "HNRNPDL" "DCLK1" "DHX15" "RNMT" "PHGDH" "NDUFS4"
[145] "DYNC1LI2" "PSMD3" "HNRNPR" "TPD52L2" "TGOLN2" "TIMM44" "CHMP2A" "NCK2" "BUB3"
[154] "AHCYL1" "SPAG9" "OPA1" "SNAP91" "PLIN3" "USO1" "PRAF2" "EIF5B" "NDUFS2"
[163] "SH3BGRL" "FLNB" "SEC22B" "KHDRBS3" "SF3B1" "CRTAP" "SLC25A12" "IDH1" "CPD"
[172] "SEC24D" "UFL1" "ABCA8" "GLS" "AP2A2" "RTN3" "FKBP9" "PGLS" "NTN1"
[181] "GSR" "COX2" "HP" "KRAS" "TFRC" "CSTB" "TUBB4A" "RPN1" "FGF1"
[190] "RPLP0" "PRKCB" "KRT8" "CALB1" "GPI" "ITGAV" "EPRS1" "CTSB" "HSP90AA1"
[199] "PFKM" "SNRPB2" "RPSA" "SNRPA" "PARP1" "LTA4H" "ALDOC" "RO60" "GAA"
[208] "H1-4" "CLU" "PYGB" "LAMP1" "G6PD" "PCNA" "NEFH" "CKMT1B" "CKMT1A"
[217] "XRCC5" "COX7A2" "HSP90B1" "DARS1" "ARSA" "RPS2" "CBR1" "GLB1" "ATP2A2"
[226] "CD36" "DES" "RPL35A" "ATP2B1" "ANXA7" "RAB3A" "PTMS" "ATP6V1C1" "SDHB"
[235] "SCP2" "IGFBP4" "SFPQ" "RPS3" "AHCY" "RPS12" "ATP5F1A" "PSMA3" "ITGB8"
[244] "DDX6" "CTNNA2" "RPL13" "EEF1G" "CNTFR" "RPL10" "MAP4" "GRN" "TKT"
[253] "PRDX5" "RPL12" "CORO1A" "DNAJA1" "HNRNPH3" "YWHAB" "L1CAM" "SLC8A1" "RPL9"
[262] "HSPA4" "CTNNA1" "NF2" "RPL22" "FUS" "ATP5F1C" "ATP6V1E1" "SRP14" "TALDO1"
[271] "GGCX" "ATP6V1A" "RPL3" "NNMT" "USP8" "MDH2" "RPL35" "PAFAH1B1" "MCAM"
[280] "CRAT" "NSF" "RPS9" "RPS10" "IQGAP1" "STT3A" "ARCN1" "LSS" "NES"
[289] "LMAN1" "ACADVL" "TMED10" "GMPS" "GNAQ" "ANXA11" "SSR4" "BCAP31" "CLCN7"
[298] "HNRNPA3" "HNRNPM" "RAP1GDS1" "GTF2A1" "SMS" "MRPL12" "HMGA2" "ACLY" "COPB1"
[307] "COPA" "ATP1B3" "RAD23B" "NAGLU" "PSMD4" "OXCT1" "MARS1" "EIF6" "H2BC5"
[316] "GNG2" "CD81" "TPI1" "EIF4A1" "RPS20" "SNAP25" "RAP1B" "RPL26" "NUTF2"
[325] "HNRNPK" "RPS7" "PPP1CB" "RPS18" "RPS11" "SNRPG" "SNRPD1" "SNRPD3" "RPL7A"
[334] "RPS4X" "ACTA2" "RHOB" "VSNL1" "RPL30" "RPL31" "GNAI1" "RPS21" "RACK1"
[343] "TPM4" "EEF1A1" "TUBA4A" "PAFAH1B2" "CXADR" "CNTNAP1" "PRKDC" "NUCB2" "BASP1"
[352] "RPL24" "PURA" "FKBP3" "DR1" "TOP2B" "ACY1" "LMNB2" "DNM1" "EEF1A2"
[361] "PSME1" "CKAP4" "KHDRBS1" "PPP3CA" "DHX9" "MFGE8" "SF3A3" "ILF3" "LMAN2"
[370] "CBX3" "SRSF9" "PABPC4" "OS9" "TUBB3" "CUL3" "EIF3A" "MVP" "GOLGB1"
[379] "SEPTIN2" "PAFAH1B3" "RAB35" "TMED2" "PCBP1" "ELOC" "SF3B3" "SAFB" "SHH"
[388] "MAPRE2" "HMGN3" "EFNB3" "SEPTIN7" "ADRM1" "INA" "DDB1" "ATP2B3" "H2AC20"
[397] "H2BC21" "P3H1" "LSM12" "PREPL" "XKR4" "MIA3" "FHL1" "ATAT1" "CEP170"
[406] "ODR4" "SF3B4" "Q5T0I0" "FKBP15" "NBEA" "SRSF11" "LSM14B" "HEXB" "CD276"
[415] "MRPL14" "PRPF8" "PLPPR3" "NSMF" "PPP1R21" "TUBA1A" "SND1" "TRMT10C" "DGLUCY"
[424] "COMMD6" "NECAB2" "PRRT2" "CENPV" "PRUNE1" "SETD3" "NT5DC3" "MB21D2" "CCAR2"
[433] "GPD1L" "ARFGAP1" "ARL10" "GATD1" "ATAD1" "LEMD2" "NUP43" "CMAS" "NPLOC4"
[442] "PLPPR1" "STT3B" "BRSK1" "HNRNPLL" "SCG3" "CTNNBL1" "HTRA1" "HDAC2" "NEO1"
[451] "KHSRP" "MYDGF" "ERGIC1" "SLC25A46" "PBXIP1" "VPS33A" "FKBP10" "FAF2" "NTNG2"
[460] "DNAJC19" "RBMXL1" "DYNLL2" "VMP1" "MAP6" "MBOAT7" "VPS35" "PURB" "PANX1"
[469] "RUFY1" "PSMD1" "PFDN5" "NAP1L4" "NIPSNAP1" "PRXL2A" "ERP44" "FSD1" "PAXX"
[478] "DPCD" "TPPP3" "MRPL9" "NLN" "NSD3" "API5" "NAT10" "COMMD4" "NAPB"
[487] "NUCKS1" "RBSN" "EHD4" "TMX1" "RPAP3" "ACAD9" "GORASP2" "MRPL44" "C17orf75"
[496] "XPO5" "GRPEL1" "VAT1L" "PLXNA4" "LZTFL1" "PLCB1" "GPHN" "FARSB" "IARS2"
[505] "ATAD3A" "SLTM" "CMTM6" "P4HTM" "CACNA2D2" "SMPD3" "PODXL2" "STMN3" "IGF2BP1"
[514] "THYN1" "NDUFAF4" "NDUFA13" "RAI14" "MACROH2A2" "TMEM63C" "GNG12" "HDAC6" "MPZL1"
[523] "SRP68" "DPP7" "NRBP1" "ATP5IF1" "CNOT7" "FBXO2" "CDV3" "ASAP1" "CADPS"
[532] "PROCR" "PHF24" "DNM3" "PA2G4" "VDAC3" "CNPY2" "WDR37" "EPB41L3" "NOP58"
[541] "LSM2" "SF3B6" "FIS1" "RTCB" "TLN1" "DAAM1" "ATP6V1D" "TIMM13" "GMPPB"
[550] "COPG1" "FKBP7" "IGF2BP2" "ERC1" # bright genome overlap
contrast_list <- c("IGSF9B-KO", "INPP5F-KO","IP6K2-KO","SH3GL2-KO") # Specify the contrasts of interest
result.dark <- get_gene_overlap_and_unique(filtered_DEP, contrast_list)
# Print results
print(result.dark$`Overlapping Genes`) # Genes common across all specified contrasts [1] "PCP4L1" "SYNM" "CSPG5" "UTS2" "TH" "CSRP1" "VAMP1" "MAOB" "NOVA1" "GPM6A" "DYNLT3" "EPHB1" "SNCB" "CARTPT" "PDK4"
[16] "LBH" "SV2A" "FAM162A" "LSM14B" "SLC6A17" "PSD3" "NTM" "SLC17A6" "GNG12" "SUN2" "PHF24" #print(result.dark$`Unique Genes`) # List of genes unique to each contrast
Look at overlap in contrasts that also have targeted pathways changes that match
print(result.THdown$`Overlapping Genes`) # Genes common across all specified contrasts
[1] "EPHB1" "PSD3" "PYCR1" "NCAM2" "SLC16A3" "RTN3" "HP" "FTL" "HSPB1" "TH" "ALDOC" "MAOA" "MAOB"
[14] "CALR" "ACLY" "CACNA2D1" "HADHB" "RAP1B" "VSNL1" "ALCAM" "COTL1" "SV2A" "CNDP2" "SYT4" "CACNA2D2" "NTM"
[27] "RRBP1" "FBXO2"
plot_protein_heatmap_zscore(
data = df,
proteins = result.THdown$`Overlapping Genes`, # Example protein names
sample_patterns = c("Control","PRKN.KO","IGSF9B" ,"SH3GL2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
scale_values = c(-1, -0.5,-0.25, 0, 1, 2.5, 4.5), # Adjust based on your data range
group_means = TRUE,# Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IGSF9B.KO.1, IGSF9B.KO.2, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
Function to plot grouped by expression
# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
data = df,
proteins = result.THdown$`Overlapping Genes`, # Example protein names
sample_patterns = c("Control", "IGSF9B.KO", "SH3GL2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values =c(-1, -0.5,-0.25, 0, 1, 2.5, 4.5), # Adjust based on your data range
group_means = FALSE, # Set to FALSE if you want individual samples
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IGSF9B.KO.1, IGSF9B.KO.2, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
data = df,
proteins = result.THdown$`Overlapping Genes`, # Example protein names
sample_patterns = c("Control", "IGSF9B.KO", "SH3GL2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1, -0.5,-0.25, 0, 1, 2.5, 4.5), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
cell_width = 20, # Control column width
cell_height = 10 # Control row height
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IGSF9B.KO.1, IGSF9B.KO.2, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
data = df,
proteins = result.THdown$`Overlapping Genes`, # Example protein names
sample_patterns = c("Control", "IGSF9B.KO", "SH3GL2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1, -0.5,-0.25, 0, 1, 2.5, 4.5), # Adjust based on your data range
group_means = FALSE, # Set to FALSE if you want individual samples
cell_width = 20, # Control column width
cell_height = 10 # Control row height
)[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IGSF9B.KO.1, IGSF9B.KO.2, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
contrast_list <- c("PRKN-KO","IGSF9B-KO","SH3GL2-KO", "IP6K2-KO") # Specify the contrasts of interest
result.GCasedown <- get_gene_overlap_and_unique(filtered_DEP, contrast_list)
# Print results
print(result.GCasedown$`Overlapping Genes`) # Genes common across all specified contrasts[1] "EPHB1" "PSD3" "TH" "MAOB" "CACNA2D1" "SV2A" "NTM" Overlapping list of the genotypes with GCAse activity down: GBA-KO, PRNK-KO, INPP5F-KO, SH3GL2-KO, IP6K2-KO
# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
data = df,
proteins = result.GCasedown$`Overlapping Genes`, # Example protein names
sample_patterns = c("Control", "IGSF9B.KO", "SH3GL2.KO","IP6K2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1.5, -1,-0.5, 0, 1, 2, 2.5), # Adjust based on your data range
group_means = FALSE, # Set to FALSE if you want individual samples
cell_width = 20, # Control column width
cell_height = 10 # Control row height
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IGSF9B.KO.1, IGSF9B.KO.2, IP6K2.KO.1, IP6K2.KO.2, IP6K2.KO.4, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
pro.list <- result.lyso$`Overlapping Genes`
# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
data = df,
proteins = pro.list, # Example protein names
sample_patterns = c("Control", "IGSF9B.KO", "SH3GL2.KO","IP6K2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-0.5,-0.25, 0, 2.5, 4.5, 6.5), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
cell_width = 20, # Control column width
cell_height = 10 # Control row height
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IGSF9B.KO.1, IGSF9B.KO.2, IP6K2.KO.1, IP6K2.KO.2, IP6K2.KO.4, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
data = df,
proteins = pro.list, # Example protein names
sample_patterns = c("Control", "IGSF9B.KO", "SH3GL2.KO","IP6K2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-0.5,-0.25, 0, 2.5, 4.5, 6.5), # Adjust based on your data range
group_means = FALSE, # Set to FALSE if you want individual samples
cell_width = 20, # Control column width
cell_height = 10 # Control row height
)[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IGSF9B.KO.1, IGSF9B.KO.2, IP6K2.KO.1, IP6K2.KO.2, IP6K2.KO.4, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
lysome <- read_excel("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/List of genes for RNAseq or Proteomics/LYSOSOME_GENE LIST.xlsx")Gene list
plot_protein_heatmap_zscore(
data = df,
proteins = lysome$`Gene name`, # Example protein names
sample_patterns = c("Control","INPP5F.KO","SH3GL2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
scale_values = c(-1, -0.5,-0.25, 0, 1, 2.5, 5.5), # Adjust based on your data range
group_means = TRUE,# Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, INPP5F.KO.1, INPP5F.KO.2, INPP5F.KO.3, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
get overlap lists
colnames(df)
[1] "Symbol" "Control.1" "Control.2" "Control.3" "Control.4" "IGSF9B.KO.1" "IGSF9B.KO.2" "INPP5F.KO.1" "INPP5F.KO.2" "INPP5F.KO.3"
[11] "IP6K2.KO.1" "IP6K2.KO.2" "IP6K2.KO.4" "SH3GL2.KO.1" "SH3GL2.KO.2" "SH3GL2.KO.3"
contrast_list <- c("INPP5F-KO","SH3GL2-KO","IP6K2-KO","IGSF9B-KO") # Specify the contrasts of interest
dark.ol <- get_gene_overlap_and_unique(filtered_DEP, contrast_list)
print(dark.ol)
$`Overlapping Genes`
[1] "PCP4L1" "SYNM" "CSPG5" "UTS2" "TH" "CSRP1" "VAMP1" "MAOB" "NOVA1" "GPM6A" "DYNLT3" "EPHB1" "SNCB" "CARTPT" "PDK4"
[16] "LBH" "SV2A" "FAM162A" "LSM14B" "SLC6A17" "PSD3" "NTM" "SLC17A6" "GNG12" "SUN2" "PHF24"
$`Unique Genes`
$`Unique Genes`$`INPP5F-KO`
[1] "PIGBOS1" "CLIC1" "STX16" "U2SURP" "NACAD" "XPOT" "STX6" "SIN3B" "DAB1" "TUSC2" "ACTL6B" "ZRANB2" "BAG3" "CFB" "ASS1"
[16] "P01861" "APOH" "ALB" "TF" "HPX" "FUCA1" "S100B" "SNRNP70" "SLC2A1" "SCG2" "UBTF" "POLR2E" "PPIB" "AK4" "CRABP1"
[31] "ERP29" "GCHFR" "SHMT2" "GARS1" "NSG1" "SLC1A4" "SMS" "SUB1" "MANF" "TRA2B" "NUCB2" "SRSF3" "SRSF2" "PPARD" "CD47"
[46] "FLII" "SRSF9" "SRSF6" "SQSTM1" "PRP4K" "IDI1" "SAFB2" "SMC1A" "CHD4" "RNPS1" "TERF2" "PTPRN" "MAP7D1" "HP1BP3" "UBR4"
[61] "ARFGEF3" "CD276" "ARSK" "ARMCX2" "NDUFAF7" "MICALL1" "PRUNE2" "COPS9" "KCTD12" "RBM17" "PPP1R9B" "CNN2" "SF3B5" "C1QTNF4" "SPRY4"
[76] "PNN" "GHITM" "EHMT1" "GMPR2" "TAGLN3"
$`Unique Genes`$`SH3GL2-KO`
[1] "AIP" "GTPBP1" "MGRN1" "LY6H" "CNTN5" "KBTBD11" "SLC25A4" "POLR2C" "ATP2B1" "GLRX" "ALDH9A1" "ALDH7A1" "BCAM"
[14] "HSD17B4" "KPNA1" "TPD52" "NCALD" "PAFAH1B2" "GPM6B" "GRM7" "ETFDH" "FRY" "TBC1D9B" "ZC3HAV1" "ARHGAP12" "GDPD1"
[27] "NUDT10" "ITFG1" "MICAL1" "MYDGF" "SH3KBP1" "CHAMP1" "LYSMD1" "TSPAN18" "HSD17B10" "OLFM1" "DNAJC5" "HEBP1" "ABCB8"
[40] "TMEM30A" "GDE1" "GPRC5B" "EHD3" "CACFD1" "SEPTIN9" "GRID1" "NFU1" "GAB2" "COPG1"
$`Unique Genes`$`IP6K2-KO`
[1] "PDLIM1" "EDIL3" "BRD4" "WDR1" "PRKCB" "NEFH" "PRKCA" "PLCG1" "ITGA6" "NF2" "FDFT1" "MTIF2" "DLG4" "EPS8" "RAPGEF1"
[16] "CIRBP" "FKBP8" "CYP51A1" "PRPF40B" "SUGP2" "SCAI" "SYNE1" "PLPPR1" "FBXO41" "SPOCK2" "EVI5L" "FMNL2" "FYTTD1" "CAMK2N2" "SRCIN1"
[31] "NT5DC2" "SERINC1" "RPRM" "TES" "OGDHL" "TDRKH" "TMX2"
$`Unique Genes`$`IGSF9B-KO`
[1] "ESYT2" "SHTN1" "ILVBL" "DENND3" "PALM3" "CASTOR2"
[7] "FAM171A2" "MCRIP1" "SLC35A4" "DNASE2" "KIF2A" "ACOT7"
[13] "MYO1C" "DFFA" "RTCA" "MANBA" "SDCBP" "RNASET2"
[19] "PODXL" "MAN2B1" "PDXK" "ARID1A" "SDHD" "DPYSL4"
[25] "COX7A2L" "UBFD1" "ABLIM1" "ADAM10" "EI24" "TP53I11"
[31] "PDCD5" "TPP1" "CPLX1" "PSMA7" "LIN7A" "TIMM23"
[37] "CASK" "UQCRQ" "CLGN" "SYN3" "SPTBN2" "KIF3B"
[43] "DCLK1" "SNPH" "DEGS1" "SCAMP2" "PGRMC2" "PFDN6"
[49] "RER1" "SURF4" "SPTLC1" "HMGB3" "STX7" "YKT6"
[55] "FABP7" "RNMT" "SEPTIN4" "DYNC1LI2" "SRGAP3" "TXNL1"
[61] "TPD52L2" "SYNJ1" "PROM1" "TGOLN2" "DENR" "DCX"
[67] "NCK2" "TSPAN6" "ZNF207" "NDUFB5" "NDUFB3" "AP1G1"
[73] "SGTA" "SLC25A20" "NUDT21" "CALU" "EXTL3" "MFSD11"
[79] "SPAG9" "KIF5C" "ACSL4" "SYNCRIP" "SELENOF" "TSPAN2"
[85] "TSPAN3" "SNAP91" "DPM1" "TIMM17B" "PRAF2" "CUTA"
[91] "PFDN1" "ABCB7" "SRGAP2" "DNAJC6" "ATP9A" "CLASP2"
[97] "PPFIA3" "TBCA" "MPDU1" "MACROH2A1" "PEX14" "NDUFB1"
[103] "ERLIN1" "CLN5" "HSBP1" "BANF1" "TIPRL" "PPM1B"
[109] "ADAP1" "PALM" "ATP6AP2" "EIF3G" "EIF3J" "ZMPSTE24"
[115] "IDH1" "ATRN" "DGAT1" "PAK3" "ARL6IP5" "FLOT1"
[121] "TRIO" "ATP5MG" "GLRX3" "RSL1D1" "SNCG" "DDAH1"
[127] "B3GAT3" "DDHD2" "NFASC" "TMCC1" "ERLIN2" "ENDOD1"
[133] "GLS" "WDR47" "AP2A2" "CLSTN1" "YIF1A" "NDUFB4"
[139] "SBF1" "VAPB" "SNAPIN" "NDUFC2" "FKBP9" "PGLS"
[145] "SEC24A" "OXSR1" "GGPS1" "BAG2" "DDAH2" "TXNDC12"
[151] "NDUFB10" "TOMM40" "PEX11B" "COX2" "HPRT1" "AK1"
[157] "ATP6" "HRAS" "IGF2" "LMNA" "APOE" "APOC3"
[163] "TFRC" "FTH1" "GBA1" "ALDOA" "CSTB" "OAT"
[169] "TUBB4A" "HLA-A" "RPN1" "RPN2" "PCCA" "PCCB"
[175] "SCG5" "ATP5MC1" "ITGB1" "GLA" "PTMA" "GPI"
[181] "TPM3" "HEXA" "EPHX1" "LDHB" "NEFL" "NEFM"
[187] "P4HB" "H1-0" "ACYP1" "CTSD" "ANXA2" "TUBB"
[193] "PSAP" "HEXB" "CTSB" "LAMB1" "ANXA6" "SYP"
[199] "ANXA5" "SNRPA" "ENO2" "GNAO1" "CLTA" "ANXA4"
[205] "COX6C" "UCHL1" "SMIM13" "RAP2A" "GAA" "H1-4"
[211] "TXN" "CTSA" "MAPT" "CHGA" "HSPD1" "CLU"
[217] "HSPA5" "ACP2" "MAP2" "PDHB" "DBT" "RALA"
[223] "RALB" "LAMP1" "TOP2A" "G6PD" "UBL4A" "IGF2R"
[229] "PCNA" "COL6A1" "CKMT1B" "CKMT1A" "ACTN1" "PEPD"
[235] "LAMP2" "NCAM1" "ATP1A3" "P4HA1" "PRKAR2A" "MIF"
[241] "FDPS" "COX7A2" "PKM" "HNRNPL" "UQCRB" "AKR1B1"
[247] "ARSA" "UCHL3" "CD46" "GNS" "ARSB" "RPA2"
[253] "COX7C" "GLB1" "PPP3CB" "H1-5" "H1-3" "H1-2"
[259] "CPE" "STMN1" "NAGA" "GOT1" "NDUFB7" "SYN1"
[265] "DES" "GAP43" "GM2A" "SON" "GNAZ" "ANXA7"
[271] "RAB3B" "PTMS" "GSTM3" "ATP6V1B2" "ATP6V1C1" "CNR1"
[277] "SYT1" "VDAC1" "OSBP" "PCMT1" "FBL" "FDXR"
[283] "PRKACB" "UQCRC2" "GCSH" "PTPRD" "CFL1" "EEF1B2"
[289] "TNC" "GRK2" "PSMA1" "PSMA3" "PSMA4" "DDX6"
[295] "CNTFR" "YWHAQ" "MARK3" "MAP4" "CANX" "PSMA5"
[301] "PSMB6" "PSMB5" "TMOD1" "GRN" "LAP3" "IMPA1"
[307] "EPHB2" "CMPK1" "PEBP1" "CORO1A" "GDI1" "PRKAR2B"
[313] "TIA1" "UQCRC1" "YWHAB" "STIP1" "S100A11" "L1CAM"
[319] "PRDX2" "GALNS" "SDC2" "HSPA4" "PFN2" "CTNNA1"
[325] "PHB1" "MYH9" "MYH10" "ADD1" "ADD2" "BSG"
[331] "PPM1A" "HMGCL" "P36268" "ARL3" "DLST" "GPX4"
[337] "NUP62" "SNCA" "LIPA" "ATP6V1A" "COL18A1" "MDH1"
[343] "HADHA" "CETN2" "CD200" "ACTR1B" "EPS15" "CASP3"
[349] "ACAA2" "RPL35" "PRCP" "ECE1" "PAFAH1B1" "SSR1"
[355] "VDAC2" "CRK" "CRKL" "NSF" "MAP1B" "STT3A"
[361] "RAP1GAP" "CAPZB" "UQCRFS1" "TFPI2" "PIP4K2A" "PPP3CC"
[367] "CD151" "NES" "HSPA13" "MARCKSL1" "LMAN1" "AMPH"
[373] "INPP1" "HARS2" "PSMB3" "ACADVL" "TMED10" "PSEN1"
[379] "TSC2" "GSK3B" "NT5C2" "SEPHS1" "GDI2" "CPT1A"
[385] "SERPINB9" "ST13" "ANXA11" "SSR4" "HCFC1" "ALDH5A1"
[391] "PSMD7" "HDGF" "PGD" "RAP1GDS1" "GTF2A1" "KIF11"
[397] "HMGA2" "PPP5C" "MVD" "CTSC" "PTTG1IP" "SLC16A1"
[403] "RAD23A" "RAD23B" "ALDH18A1" "MFAP2" "AFDN" "CDH6"
[409] "HNRNPH2" "BID" "ARPP19" "AP1S2" "CORO7" "EPPK1"
[415] "FXYD7" "TPI1" "SEC61B" "PSMA6" "S100A10" "SPCS3"
[421] "UBE2K" "UBE2N" "RPL26" "STX1B" "RPL27" "PCBD1"
[427] "SEC61A1" "VBP1" "STXBP1" "DAD1" "NPC2" "PKIA"
[433] "SUMO2" "UFM1" "AP1S1" "YWHAG" "YWHAE" "RPS18"
[439] "RPS13" "ARF6" "PPP2CB" "RHOB" "RPS6" "RPS24"
[445] "GNB1" "RBX1" "RPL32" "PPIA" "FKBP1A" "RAC1"
[451] "VAMP2" "YWHAZ" "PPP2CA" "YBX1" "SEC11A" "UBE2L3"
[457] "TUBA4A" "TUBB4B" "CXADR" "GTF2I" "PIP4K2B" "SLC35A1"
[463] "ARG2" "BASP1" "MRPS11" "ARF5" "H3-3A" "H3-3B"
[469] "HSPG2" "CYCS" "SLC25A3" "CDK5" "CLTC" "TIAL1"
[475] "SET" "AMPD2" "CTBS" "CAP1" "HMGCS1" "DR1"
[481] "ATP2B2" "EWSR1" "OCRL" "PLOD1" "RPL6" "SLC25A11"
[487] "PTS" "MVK" "EEF1A2" "PSME1" "PRDX1" "CKAP4"
[493] "TJP1" "KLC1" "LRP1" "ARHGAP1" "PPP3CA" "GOLGA3"
[499] "LGALS3BP" "MFGE8" "DMTN" "PPID" "SCRN3" "GALNT2"
[505] "AP1B1" "KIF1A" "SCRN1" "CNTN1" "LMAN2" "ANK3"
[511] "PAK1" "PAK2" "DNAJC3" "NME3" "MAD2L1" "PTK7"
[517] "AP3B2" "UBE2V1" "PEDS1-UBE2V1" "DYNC1I2" "OS9" "PDAP1"
[523] "TMED1" "LSAMP" "MTX1" "TUBB3" "CAMK2B" "DCTN2"
[529] "STIM1" "ITGA7" "MOGS" "SPTAN1" "TUBB2A" "HNRNPD"
[535] "SCARB2" "DAG1" "MLEC" "TTLL12" "MPP2" "CRMP1"
[541] "DPYSL3" "DCTN1" "EIF4A2" "FLOT2" "FLNC" "ELAVL3"
[547] "INPP5A" "CLINT1" "GANAB" "LBR" "MVP" "SPCS2"
[553] "EMC2" "PSMD6" "SEPTIN2" "RRS1" "POSTN" "PDIA6"
[559] "PAFAH1B3" "PPP2R5B" "PTPA" "QPRT" "RABEP1" "RCN1"
[565] "TMED2" "SHH" "MAPRE2" "SF1" "MAPRE1" "EFNB3"
[571] "RAB30" "ITSN1" "TBCE" "UBE2V2" "VAMP3" "NEDD8"
[577] "INA" "CSRP2" "DPYSL2" "STX1A" "CPSF6" "SMN2"
[583] "SMN1" "DBN1" "FSCN1" "ATP2B3" "TST" "HAGH"
[589] "H2BC21" "PTPRO" "UGP2" "HNRNPUL2" "P3H1" "LSM12"
[595] "CCDC88A" "LGALSL" "VPS26B" "CCDC184" "TMEM35A" "HSD17B12"
[601] "RAB6D" "SDK2" "TMEM97" "YIF1B" "MEST" "XKR4"
[607] "NOMO2" "MIA3" "WDR44" "GNAS" "SAMD4B" "ATAT1"
[613] "CEP170" "CLVS2" "TPRG1L" "FNBP1L" "GPR158" "WLS"
[619] "Q5TF21" "MICOS10" "WASHC2A" "PPP2R2D" "MBLAC2" "LMBRD2"
[625] "IQSEC1" "CARMIL2" "RALGAPA1" "TMEM132E" "TCEAL6" "SLC25A24"
[631] "REEP3" "ARMC6" "JMJD6" "PPP1R18" "SLC48A1" "SLC27A4"
[637] "EDC4" "SCYL2" "CNNM4" "ERICH5" "MEAK7" "PGM2L1"
[643] "AAGAB" "TMEM65" "NCEH1" "CPLX2" "MOXD1" "CYP20A1"
[649] "POGLUT2" "TMEM205" "ISLR2" "APOOL" "PACS1" "RAB11FIP1"
[655] "HSDL2" "CRACD" "GPRIN3" "TOM1L2" "TMTC3" "IKBIP"
[661] "UBE2R2" "TLCD3B" "TUBA1A" "RUFY3" "EPM2AIP1" "TAOK1"
[667] "MOB1B" "CHMP1B" "MICAL3" "GPRIN1" "NUP54" "KIF21A"
[673] "PRRT2" "TMED4" "GALNT7" "PRUNE1" "MAGI2" "USP48"
[679] "NT5DC3" "CAND1" "OSTM1" "DDX42" "NDUFA11" "DOLPP1"
[685] "GPSM1" "ERC1" "NUDCD3" "SLC44A2" "BRSK2" "SULF2"
[691] "DNAJC10" "RHOT2" "SIRT2" "NRM" "SMAP1" "KIAA0319L"
[697] "CACNA2D3" "CEND1" "NUP93" "ABHD12" "SLC43A2" "CADM2"
[703] "MMGT1" "AFAP1" "CALHM5" "KCNRG" "JAGN1" "COMMD1"
[709] "EMC1" "AMER2" "GATD1" "GOLM1" "COLGALT1" "SUMF2"
[715] "TMEM87A" "TXNDC5" "LEMD2" "NECAP1" "CAMKV" "PLA2G15"
[721] "Q8NCU8" "MROH1" "EHBP1" "MCU" "NUP37" "NBEA"
[727] "NLGN2" "CADM4" "PLBD2" "GRPEL2" "FGFBP3" "UBA3"
[733] "C18orf32" "STT3B" "HM13" "GPX8" "RAPGEF6" "C16orf78"
[739] "PDCD6IP" "PSPC1" "PPM1E" "JDP2" "LZIC" "IRGQ"
[745] "DDX1" "H1-10" "NCSTN" "TM9SF4" "WASF1" "SLC9A6"
[751] "NDRG1" "NUP205" "TTC9" "SORL1" "ARPC1A" "GGH"
[757] "NRCAM" "KIFAP3" "CELF1" "GLG1" "PTPRN2" "KHSRP"
[763] "USP9X" "STMN2" "BORCS5" "UBE2E3" "NCLN" "ERGIC1"
[769] "CCDC47" "TMEM230" "FUBP1" "VTI1A" "MCUR1" "FKBP10"
[775] "TOMM6" "ARL8A" "CCDC127" "EFHD2" "PYCR2" "DCPS"
[781] "PPP1R14B" "ISOC1" "FOXRED1" "AP2M1" "RAB39B" "CMBL"
[787] "ERLEC1" "RAB3C" "DAZAP1" "SGTB" "CYFIP2" "CNRIP1"
[793] "LINGO1" "DYNLL2" "OTUB1" "CHMP6" "CERS2" "ZC2HC1A"
[799] "CRELD1" "SFRP2" "DIRAS2" "CDK5RAP3" "TMX3" "FAM210B"
[805] "PHACTR3" "PRRC1" "GDAP1L1" "VSTM2L" "MBOAT7" "RUNDC3B"
[811] "NAP1L5" "AGAP3" "MIA2" "NEDD4L" "VPS35" "MAGI1"
[817] "PANX1" "PIGS" "SLC9A7" "YME1L1" "SCN2A" "TBCB"
[823] "SEC62" "PFDN5" "PARK7" "TSC22D3" "TTC1" "PHB2"
[829] "ATXN2" "SEPTIN5" "SIGMAR1" "NAPG" "EBNA1BP2" "H2BC13"
[835] "DPYSL5" "ECSIT" "SYT3" "TXNDC17" "NUDT16L1" "SDF4"
[841] "ERP44" "ESYT1" "TMEM43" "FSD1" "FUCA2" "LMF2"
[847] "TUBB2B" "TMEM109" "PBDC1" "PTDSS2" "TMED9" "TPPP3"
[853] "ACAT2" "CHID1" "FSD1L" "AP1M1" "RAB34" "TRIM2"
[859] "SEMA4C" "TTYH3" "YIPF3" "NDEL1" "ROGDI" "PITHD1"
[865] "MAP1LC3B" "UBA5" "NAT10" "KLC2" "FXYD6" "HDHD2"
[871] "MAGT1" "LMAN2L" "NAPB" "SIL1" "NUCKS1" "SH3BGRL3"
[877] "SLC38A1" "CPVL" "DPAGT1" "POFUT1" "PIGU" "MAP1LC3A"
[883] "CLSTN2" "EPB41L1" "SMDT1" "PRR36" "REEP1" "SFXN1"
[889] "COG4" "NMNAT1" "C17orf75" "TMEM165" "GLOD4" "PCDH9"
[895] "MCCC2" "TM9SF3" "APMAP" "VTA1" "NPHS2" "DYNLRB1"
[901] "EMC7" "PLCB1" "PFDN4" "EIF2B3" "OSTC" "RAB6B"
[907] "TOMM22" "LRRC4B" "RBM12" "STAU2" "SHFL" "TMEM106B"
[913] "AGPAT5" "CYRIB" "EXOC1" "TMEM38B" "ANO10" "NDUFB11"
[919] "OCIAD1" "CHCHD3" "P4HTM" "BABAM2" "DPP3" "SCN3A"
[925] "SCN3B" "TERF2IP" "TMOD3" "UGGT1" "ERAP1" "PODXL2"
[931] "TMOD2" "CHMP5" "HACD3" "KCMF1" "RAI14" "MACROH2A2"
[937] "TBC1D7" "TBC1D7-LOC100130357" "TOMM7" "TMEM63C" "CAMSAP3" "NRXN2"
[943] "DPM3" "NCDN" "EPS15L1" "MRC2" "CPNE7" "EXTL2"
[949] "DNAJB11" "SEL1L" "PEF1" "ZMYM2" "SLC25A10" "CLIP2"
[955] "CFDP1" "SEC63" "SEPTIN3" "CHORDC1" "UBQLN2" "PCYOX1"
[961] "PFDN2" "PUF60" "NRBP1" "PLXNA1" "GGT7" "NAGK"
[967] "SH3BGRL2" "SLC25A13" "DBNL" "PURG" "GGA1" "STOML2"
[973] "JPT1" "PTBP2" "TNIK" "PITPNC1" "MAN1B1" "CDV3"
[979] "NRXN1" "SLC39A10" "ASAP1" "CORO1C" "HPCAL4" "MYO6"
[985] "UBQLN1" "SNX12" "SYNRG" "SSR3" "FAF1" "MINPP1"
[991] "NOVA2" "NSFL1C" "MAPK8IP3" "USP24" "AGTPBP1" "MAPRE3"
[997] "CORO2B" "DNM3" "NUDC" "CFL2"
[ reached getOption("max.print") -- omitted 35 entries ]specify group function to get the specific overlap list in the upset plot
print(overlap_genes)
[1] "EHD1" "B4DLN1" "CALD1" "ATP6V0D1" "APRT" "ATXN2L" "CS" "PSIP1" "NDUFS3" "TOMM70" "GOT2" "HMGN2" "FH"
[14] "VIM" "ATP5PF" "HK1" "ATP5PB" "ALDH1B1" "GK" "CBX5" "FSTL1" "PLEC" "HMGB1" "NXN" "FAHD1" "SERBP1"
[27] "NUBPL" "FUBP3" "JPT2" "SLC25A22" "PHPT1" "SEPTIN10" "UBA2" "ADD3" "NENF" "SUPT16H" length(overlap_genes)[1] 36write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/GBA_PINK1_PRKN_DPE.csv")Make list corresponding to the upset plot to save and send to Roxanne
# pink parkin
contrast_list <- c("PINK1-KO", "PRKN-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of PINK1 PRKN")[1] "Overlap of PINK1 PRKN"print(overlap_genes) [1] "PABPC1" "RDX" "MEA1" "A0A5F9UP49" "ACAT1" "SCRIB" "CAST" "EIF3E" "ADAM9" "EIF4H" "PITRM1"
[12] "SRP54" "ACSL3" "LRPPRC" "A2A2V1" "NUDT5" "TP53BP1" "SUMO3" "MATR3" "EIF3H" "MSI2" "NQO1"
[23] "EIF3CL" "SH3GLB2" "CROT" "CTNND1" "MRPS27" "EIF4B" "EIF4G1" "AGK" "OGDH" "EPB41L2" "SLC3A2"
[34] "PCBP2" "NDUFV1" "H0Y3P2" "COPB2" "NACA" "H3BN98" "TBL3" "KDSR" "IGF2BP3" "PIR" "DHX15"
[45] "NDUFS4" "PSMD3" "TIMM44" "BUB3" "OPA1" "USO1" "EIF5B" "NDUFS2" "SH3BGRL" "KHDRBS3" "NTN1"
[56] "GSR" "PARP1" "LTA4H" "RO60" "XRCC5" "CBR1" "ATP2A2" "CD36" "SDHB" "RPS12" "ATP5F1A"
[67] "RPL13" "EEF1G" "TKT" "PRDX5" "DNAJA1" "RPL9" "ATP5F1C" "SRP14" "TALDO1" "RPL3" "MDH2"
[78] "CRAT" "IQGAP1" "ARCN1" "GMPS" "BCAP31" "CLCN7" "MRPL12" "PSMD4" "OXCT1" "H2BC5" "CD81"
[89] "TPM4" "PRKDC" "PURA" "PABPC4" "EIF3A" "PCBP1" "ELOC" "SAFB" "HMGN3" "ADRM1" "DDB1"
[100] "PREPL" "ODR4" "FKBP15" "SRSF11" "NSMF" "TRMT10C" "COMMD6" "SETD3" "ARL10" "ATAD1" "NPLOC4"
[111] "SCG3" "NEO1" "VPS33A" "NTNG2" "DNAJC19" "PURB" "RUFY1" "PSMD1" "NAP1L4" "NIPSNAP1" "PRXL2A"
[122] "PAXX" "DPCD" "NLN" "NSD3" "COMMD4" "RBSN" "TMX1" "ACAD9" "GORASP2" "MRPL44" "XPO5"
[133] "GRPEL1" "LZTFL1" "IARS2" "ATAD3A" "IGF2BP1" "THYN1" "NDUFAF4" "NDUFA13" "MPZL1" "SRP68" "CNOT7"
[144] "PA2G4" "VDAC3" "CNPY2" "NOP58" "FIS1" "TIMM13" "IGF2BP2" length(overlap_genes)[1] 150write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/PINK1_PRKN_DPE.csv")
print("saved")[1] "saved"# PRKN IGSF9B
contrast_list <- c("IGSF9B-KO", "PRKN-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B PRKN")[1] "Overlap of IGSF9B PRKN"print(overlap_genes) [1] "RTCA" "PDXK" "CASK" "SYN3" "SURF4" "DENR" "ACSL4" "ZMPSTE24" "ATP5MG" "DDAH1" "ERLIN2" "SEC24A" "DDAH2"
[14] "AK1" "ALDOA" "PCCA" "TUBB" "HSPD1" "PDHB" "ACTN1" "PEPD" "FDPS" "UQCRB" "CPE" "CNR1" "PCMT1"
[27] "PRKACB" "UQCRC2" "PSMA4" "PSMA5" "PSMB6" "CMPK1" "PEBP1" "PRDX2" "PHB1" "ADD2" "ARL3" "VDAC2" "CRK"
[40] "CRKL" "UQCRFS1" "PSMB3" "GDI2" "PGD" "PPP5C" "CDH6" "PSMA6" "SPCS3" "UBE2K" "VBP1" "PPP2CB" "FKBP1A"
[53] "YBX1" "AMPD2" "MVK" "PRDX1" "LRP1" "AP1B1" "PAK2" "PTK7" "EIF4A2" "GANAB" "PTPA" "RABEP1" "NEDD8"
[66] "DBN1" "FSCN1" "TMEM35A" "SLC25A24" "CACNA2D3" "AFAP1" "GRPEL2" "IRGQ" "ARPC1A" "AP2M1" "PARK7" "CHID1" "FSD1L"
[79] "TTYH3" "FXYD6" "CPVL" "ANO10" "CHCHD3" "TMOD2" "EPS15L1" "DBNL" "STOML2" "PTBP2" "SLC39A10" "CFL2" length(overlap_genes)[1] 90write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_PRKN_DPE.csv")
print("saved")[1] "saved"# IGSF9B pink parkin
contrast_list <- c("PINK1-KO", "PRKN-KO", "IGSF9B-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B PINK1 PRKN")[1] "Overlap of IGSF9B PINK1 PRKN"print(overlap_genes) [1] "TPM3" "TMEM132E" "DNAJC10" "CSRP2" "TJP1" "ZNF207" "TPD52L2" "NCK2" "PRAF2" "GLS" "FKBP9" "PGLS" "COX2"
[14] "TFRC" "CSTB" "CTSB" "CLU" "LAMP1" "CKMT1B" "CKMT1A" "COX7A2" "ARSA" "PTMS" "ATP6V1C1" "PSMA3" "CNTFR"
[27] "L1CAM" "CTNNA1" "NES" "ANXA11" "RAP1GDS1" "GTF2A1" "RAD23B" "TPI1" "RPS18" "PSME1" "PPP3CA" "SEPTIN2" "PAFAH1B3"
[40] "MAPRE2" "EFNB3" "H2BC21" "ATAT1" "CEP170" "STT3B" "KHSRP" "ERGIC1" "DYNLL2" "PANX1" "PFDN5" "TPPP3" "NAPB"
[53] "C17orf75" "PLCB1" "P4HTM" "PODXL2" "TMEM63C" "CDV3" "RTCB" length(overlap_genes)[1] 59write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_PINK1_PRKN_DPE.csv")
print("saved")[1] "saved"# PRKN IGSF9B
contrast_list <- c("IGSF9B-KO", "PINK1-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B PINK1")[1] "Overlap of IGSF9B PINK1"print(overlap_genes) [1] "LIN7A" "DCLK1" "RNMT" "DYNC1LI2" "TGOLN2" "SPAG9" "SYNCRIP" "IDH1" "AP2A2" "GPI" "SNRPA" "H1-4"
[13] "DES" "DDX6" "MAP4" "GRN" "CORO1A" "YWHAB" "SDC2" "HSPA4" "GPX4" "ATP6V1A" "RPL35" "PAFAH1B1"
[25] "LMAN1" "HMGA2" "TUBA4A" "CLTC" "DR1" "EEF1A2" "MFGE8" "AP3B2" "MOGS" "SHH" "ATP2B3" "LSM12"
[37] "XKR4" "MIA3" "TUBA1A" "PRUNE1" "ERC1" "GATD1" "LEMD2" "NBEA" "CRELD1" "VPS35" "ERP44" "FSD1"
[49] "TRIM2" "RAI14" "MACROH2A2" "SEPTIN3" "NRBP1" "DNM3" length(overlap_genes)[1] 54write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_PINK1_DPE.csv")
print("saved")[1] "saved"# INPP5F IGSF9B
contrast_list <- c("IGSF9B-KO", "INPP5F-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B INPP5F")[1] "Overlap of IGSF9B INPP5F"print(overlap_genes) [1] "SYT5" "AGRN" "ASTN1" "GPAA1" "ECI2" "ADGRL2" "BCHE" "ITGA5" "LAMC1" "TOP1" "GJA1" "M6PR" "COL5A1"
[14] "MDK" "IGFBP5" "MCM3" "CCN2" "CLCN6" "H4C8" "H4C9" "H4C6" "H4C12" "H4C5" "H4C13" "H4C1" "H4C4"
[27] "H4C2" "H4C16" "SPTBN1" "BAX" "ASPH" "SYPL1" "EMC4" "TOR1AIP1" "ATRAID" "RAPH1" "H3C14" "H3C15" "H3C13"
[40] "HS2ST1" "H2AC21" "SRRM1" "TOMM5" "NUP210" "BRI3BP" "SEMA4D" "TSNAX" "GDF15" "RAB5IF" "FUT8" "MTCH1" "CNTNAP2"
[53] "NDRG4" "PPM1H" "THSD7A" "UCHL5" "GPC6" "CLIC4" length(overlap_genes)[1] 58write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_INPP5F_DPE.csv")
print("saved")[1] "saved"# INPP5F SH3GL2
contrast_list <- c("SH3GL2-KO", "INPP5F-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of SH3GL2 INPP5F")[1] "Overlap of SH3GL2 INPP5F"print(overlap_genes) [1] "RAB27B" "SCD" "MID1" "MYO1B" "NOL3" "EML2" "AGR2" "CST3" "S100A6" "ABCB1" "MTHFD2" "ANK1" "MSN"
[14] "CLIP1" "SERPINB6" "CAP2" "TMPO" "NPTX2" "P51784" "MAP2K1" "GFPT1" "SRSF5" "ITIH4" "OPCML" "DLG2" "PTGIS"
[27] "PCK2" "ALDH1L2" "MDGA2" "AHNAK2" "Q8IXS6" "CMIP" "FAM177A1" "GPC2" "GPT2" "SNRNP27" "ARHGEF2" "WBP2" "SPOCK3"
[40] "FAM107B" "CYSTM1" "ALG2" "POMK" "DCTPP1" "ZCCHC3" "ARMCX1" "XPR1" "DNAJB4" "PCDHA4" "FNDC3A" "HYOU1" length(overlap_genes)[1] 51write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SH3GL2_INPP5F_DPE.csv")
print("saved")[1] "saved"# SNCA A53T pink parkin
contrast_list <- c("PINK1-KO", "PRKN-KO", "SNCA-A53T")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of SNCA A53T PINK1 PRKN")[1] "Overlap of SNCA A53T PINK1 PRKN"print(overlap_genes) [1] "MBNL1" "PON2" "PGM3" "GDAP1" "NONO" "SLC1A3" "PALM2AKAP2" "SEC31A" "TNPO3" "E9PJP2" "RPL28"
[12] "BET1" "RPS5" "RPL21" "RPS16" "GOLIM4" "CRTAP" "SLC25A12" "CALB1" "RPSA" "RPS2" "RPL35A"
[23] "SCP2" "RPS3" "AHCY" "ITGB8" "RPL10" "GGCX" "NNMT" "RPS10" "GNAQ" "COPB1" "COPA"
[34] "ATP1B3" "GNG2" "EIF4A1" "RPS20" "SNAP25" "RPS7" "RPS4X" "RPL30" "GNAI1" "RACK1" "EEF1A1"
[45] "CNTNAP1" "RPL24" "FKBP3" "DHX9" "SND1" "HNRNPLL" "RBMXL1" "MRPL9" "VAT1L" "CMTM6" "ATP5IF1"
[56] "PROCR" "GMPPB" length(overlap_genes)[1] 57write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SNCAA53T_PINK1_PRKN_DPE.csv")
print("saved")[1] "saved"# bright genome and IGSF9B
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of SNCA GBA PINK1 PRKN IGSF9B")[1] "Overlap of SNCA GBA PINK1 PRKN IGSF9B"print(overlap_genes) [1] "NCAM1" "STXBP1" "PALM3" "P4HB" "CYCS" "CTSD" "SNCA" "NEFM" "TP53I11" "DCX" "CLN5" "SNCG" "HRAS"
[14] "NEFL" "ANXA2" "SYP" "ANXA5" "GNAO1" "HSPA5" "RALA" "ATP1A3" "P4HA1" "GNS" "PPP3CB" "SYN1" "GAP43"
[27] "EPHB2" "ACAA2" "HDGF" "RPS13" "GNB1" "SET" "PLOD1" "LGALS3BP" "GALNT2" "CNTN1" "DNAJC3" "STX1A" "GDAP1L1"
[40] "CEND1" "CADM2" "GLG1" "CMBL" "DIRAS2" "FUCA2" "TMOD3" "PLXNA1" length(overlap_genes)[1] 47write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SNCAA53T_GBA_PINK1_PRKN_IGSF9B_DPE.csv")
print("saved")[1] "saved"# bright genome
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of SNCA GBA PINK1 PRKN")[1] "Overlap of SNCA GBA PINK1 PRKN"print(overlap_genes) [1] "HDLBP" "A0A0J9YYL3" "A0A1P0AYU5" "RPS14" "HSPA12A" "OBSCN" "SNRPE" "SCFD1" "RPS3A" "F8W6I7" "CD44"
[12] "H3BQZ7" "CBX1" "HNRNPUL1" "KPNA6" "SSB" "PYGL" "DCN" "NCL" "HNRNPA2B1" "ATP2B4" "FKBP2"
[23] "EEF1D" "PRDX6" "RPS19" "SRP9" "RPS8" "RPS23" "RPL23A" "RPL10A" "ABAT" "ERH" "NUCB1"
[34] "CRYZ" "AHNAK" "TRIM28" "FNDC3B" "GOLPH3" "CTPS2" "SEC23IP" length(overlap_genes)[1] 40write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SNCAA53T_GBA_PINK1_PRKN_DPE.csv")
print("saved")[1] "saved"
# bright genome
contrast_list <- c("SH3GL2-KO", "INPP5F-KO","IGSF9B-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of SH3GL2 INPP5F IGSF9B")[1] "Overlap of SH3GL2 INPP5F IGSF9B"print(overlap_genes) [1] "APBB1" "PCDH7" "SLIT2" "ATP6V1G2" "PCSK1" "DCC" "EPHA4" "PLTP" "GFRA1" "ANK2" "APBA1" "PCDH1" "CAMK2G"
[14] "PLCB4" "DECR1" "BRINP3" "NEGR1" "ACOT1" "CNTN4" "NLGN4X" "IGSF1" "DLG3" "CCDC51" "SERPINI1" "EDEM3" "CHODL"
[27] "CRTAC1" "NLGN3" "MYOF" "SEMA5B" "EPHA6" length(overlap_genes)[1] 31write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_INPP5F_SH3GL2_DPE.csv")
print("saved")[1] "saved"# IGSF9B pink parkin SNCA
contrast_list <- c("PINK1-KO", "PRKN-KO", "IGSF9B-KO","SNCA-A53T")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B PINK1 PRKN SNCA-A53T")[1] "Overlap of IGSF9B PINK1 PRKN SNCA-A53T"print(overlap_genes) [1] "TMED7-TICAM2" "SCARB2" "RPS24" "UGP2" "PTPRD" "NPC2" "PTMA" "FXYD7" "RPN1" "GAA"
[11] "G6PD" "PCNA" "GLB1" "ANXA7" "STT3A" "TMED10" "SSR4" "BASP1" "LMAN2" "INA"
[21] "P3H1" "HEXB" "NT5DC3" "FKBP10" "MBOAT7" "NUCKS1" "ASAP1" "TLN1" "FKBP7" length(overlap_genes)[1] 29write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SNCAA53T_IGSF9B_PINK1_PRKN_DPE.csv")
print("saved")[1] "saved"contrast_list <- c("PINK1-KO", "PRKN-KO", "IGSF9B-KO","GBA-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B PINK1 PRKN GBA")[1] "Overlap of IGSF9B PINK1 PRKN GBA"print(overlap_genes) [1] "VTA1" "LMNA" "PRCP" "MAN2B1" "GLRX3" "OAT" "NAGA" "VDAC1" "HMGCS1" "PDAP1" "FLNC" "CELF1" "RAB3C"
[14] "NAPG" "MAP1LC3A" "SLC25A13" "SAMM50" length(overlap_genes)[1] 17write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/GBA_IGSF9B_PINK1_PRKN_DPE.csv")
print("saved")[1] "saved"
# A53T IGSF9B
contrast_list <- c("IGSF9B-KO", "SNCA-A53T")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B SNCA A53T")[1] "Overlap of IGSF9B SNCA A53T"print(overlap_genes) [1] "SDCBP" "DNAJC6" "PALM" "PAK3" "CLSTN1" "ACYP1" "UCHL1" "MAPT" "STMN1" "CANX" "PRKAR2B" "PFN2" "PPP3CC"
[14] "MARCKSL1" "RAC1" "TUBB2A" "CRMP1" "DPYSL3" "GNAS" "SLC27A4" "RHOT2" "CHMP6" "ZC2HC1A" "TMX3" "TM9SF3" "GGT7"
[27] "NRXN1" length(overlap_genes)[1] 27write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_SNCAA53T_DPE.csv")
print("saved")[1] "saved"# A53T IGSF9B
contrast_list <- c("IGSF9B-KO", "SH3GL2-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B SH3GL2")[1] "Overlap of IGSF9B SH3GL2"print(overlap_genes) [1] "KPNA4" "IDH3B" "STAMBP" "CD63" "SRC" "HMGA1" "STOM" "MARCKS" "TSPAN7" "PCBP3" "IGBP1" "TGFBI" "TSC22D1" "MAVS" "ADCK1"
[16] "SCAMP5" "CD99L2" "PDXP" "SH3GL2" "SEZ6L" "ACTR3B" "EPDR1" "SLC8A2" "TLN2" "NUMBL" length(overlap_genes)[1] 25write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_SH3GL2_DPE.csv")
print("saved")[1] "saved"# A53T IGSF9B
contrast_list <- c("IGSF9B-KO", "IP6K2-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B IP6K")[1] "Overlap of IGSF9B IP6K"print(overlap_genes) [1] "SNX4" "PENK" "SERPINE2" "NGFR" "MGST1" "PTN" "IDH3A" "SYNJ2BP" "SARNP" "TUSC3" "ANO6" "TCEAL5" "EARS2"
[14] "SNX30" "ATCAY" "THNSL1" "SHC3" "PPP1R10" "VAT1" "TM9SF2" "PACSIN1" "SLC5A7" "TBC1D24" "CAMK2A" "PPP2R2C" length(overlap_genes)[1] 25write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_IP6K2_DPE.csv")
print("saved")[1] "saved"# A53T PINK1
contrast_list <- c("PINK1-KO", "SNCA-A53T")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of PINK1KO SNCA A53T")[1] "Overlap of PINK1KO SNCA A53T"print(overlap_genes) [1] "TARDBP" "DDX17" "NPM1" "RPL8" "SEC22B" "RPLP0" "RAB3A" "SFPQ" "RPL22" "RPS9" "HNRNPM" "HNRNPK" "RPL7A" "TOP2B" "DNM1" "GOLGB1"
[17] "RAB35" "SF3B3" "Q5T0I0" "PLPPR3" "MB21D2" "CMAS" "BRSK1" "MAP6" length(overlap_genes)[1] 24write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/PINK1_SNCAA53T_DPE.csv")
print("saved")[1] "saved"# A53T PINK1 GBA
contrast_list <- c("PINK1-KO", "SNCA-A53T","GBA-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of PINK1KO SNCA A53T and GBA")[1] "Overlap of PINK1KO SNCA A53T and GBA"print(overlap_genes) [1] "HNRNPU" "RPL5" "HNRNPC" "B4E171" "HNRNPH1" "HMGN1" "RPL17" "DDX5" "HSPA4L" "CA2" "XRCC6" "SNRPB" "LMNB1"
[14] "RPL23" "NCBP1" "ATP6V0A1" "THRAP3" length(overlap_genes)[1] 17write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/PINK1_SNCAA53T_GBA_DPE.csv")
print("saved")[1] "saved"# A53T PINK1 IGSF9B
contrast_list <- c("PINK1-KO", "SNCA-A53T","IGSF9B-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of PINK1KO SNCA A53T and IGSF9B")[1] "Overlap of PINK1KO SNCA A53T and IGSF9B"print(overlap_genes) [1] "SHTN1" "SNAP91" "TUBB4A" "NSF" "ACADVL" "RPL26" "RHOB" "CXADR" "CKAP4" "OS9" "TUBB3" "MVP" "TMED2" "PRRT2" "NAT10" "WDR37" length(overlap_genes)[1] 16write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/PINK1_SNCAA53T_IGSF9B_DPE.csv")
print("saved")[1] "saved"# dark genome
contrast_list <- c("SH3GL2-KO", "INPP5F-KO","IGSF9B-KO", "IP6K2-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of SH3GL2 INPP5F IGSF9B")[1] "Overlap of SH3GL2 INPP5F IGSF9B"print(overlap_genes) [1] "PCP4L1" "SYNM" "CSPG5" "UTS2" "VAMP1" "NOVA1" "DYNLT3" "CARTPT" "PDK4" "LBH" "FAM162A" "SLC6A17" "SLC17A6" "SUN2" length(overlap_genes)[1] 14write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_INPP5F_SH3GL2_IP6K2_DPE.csv")
print("saved")[1] "saved"Individual contrasts
contrast_list <- c("SNCA-A53T")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to SNCA A53T")[1] "Unique to SNCA A53T"print(overlap_genes) [1] "ABHD6" "NUDT9" "AGAP1" "GUCY1B1" "GOSR1" "COPZ1" "DDX3Y" "NRAS" "QDPR" "ARF4" "TAGLN2" "GNG4" "EIF5"
[14] "MRPS5" "SF3B2" "XKR7" "RPL11" "ASRGL1" "PLD3" "FAM114A1" "HYCC2" "HSD17B11" "TTN" "LRRC59" "FAM241B" "SDF2"
[27] "CADM1" "UBE2O" "SAR1A" "BPNT2" "BSN" "ATP8A1" "NPTN" length(overlap_genes)[1] 33write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SNCAA53T_DPE.csv")
print("saved")[1] "saved"contrast_list <- c("GBA-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to GBA KO")[1] "Unique to GBA KO"print(overlap_genes)[1] "MADD" "IDH3G" "PLXNC1" "NNT" "RHEB" "HSDL1" "ABCF1" "RAPGEF4" "GDA" length(overlap_genes)[1] 9write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/GBA_DPE.csv")
print("saved")[1] "saved"contrast_list <- c("PINK1-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to PINK1 KO")[1] "Unique to PINK1 KO"print(overlap_genes) [1] "A0A087WTM1" "A0A087WY61" "CD99" "MYEF2" "A0A0A0MR09" "ILK" "ENAH" "QARS1" "DLG1" "TCOF1" "UBE3A"
[12] "PPIG" "RMND1" "RABGAP1L" "IARS1" "PTBP1" "ILF2" "PREB" "PLRG1" "HNRNPAB" "SKP1" "ALYREF"
[23] "CELF2" "ADCYAP1R1" "HSPA8" "LAMTOR1" "UBAP2L" "F8WE88" "ARFGAP2" "RPL36A" "TMEM199" "SRSF1" "FARSA"
[34] "RPL18A" "HIP1" "HMGN4" "HNRNPR" "CHMP2A" "AHCYL1" "PLIN3" "SF3B1" "CPD" "SEC24D" "UFL1"
[45] "ABCA8" "KRAS" "FGF1" "EPRS1" "HSP90AA1" "PFKM" "SNRPB2" "PYGB" "DARS1" "IGFBP4" "RPL12"
[56] "HNRNPH3" "FUS" "ATP6V1E1" "USP8" "LSS" "HNRNPA3" "MARS1" "EIF6" "NUTF2" "PPP1CB" "RPS11"
[67] "SNRPG" "SNRPD1" "SNRPD3" "ACTA2" "RPL31" "ACY1" "LMNB2" "KHDRBS1" "SF3A3" "ILF3" "CBX3"
[78] "CUL3" "SEPTIN7" "H2AC20" "SF3B4" "MRPL14" "PRPF8" "PPP1R21" "DGLUCY" "CENPV" "CCAR2" "GPD1L"
[89] "NUP43" "CTNNBL1" "HDAC2" "SLC25A46" "FAF2" "VMP1" "API5" "EHD4" "RPAP3" "FARSB" "SLTM"
[100] "HDAC6" "CADPS" "LSM2" "SF3B6" "DAAM1" "ATP6V1D" length(overlap_genes)[1] 105write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/PINK1_DPE.csv")
print("saved")[1] "saved"contrast_list <- c("PRKN-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to PRKN KO")[1] "Unique to PRKN KO"print(overlap_genes) [1] "HMOX2" "A0A087WTF6" "A0A087WUC6" "MTHFD1L" "PSME2" "PSMC6" "ELAVL2" "ATP6AP1" "HSPA1B" "HSPA1A" "KAZN"
[12] "BRAF" "CTNNB1" "SUCLA2" "RAPGEF2" "PDE10A" "ABCD3" "CLUH" "IMPDH2" "NDUFA10" "ADSL" "NAXD"
[23] "CUL4B" "AASS" "DLAT" "SH3PXD2B" "ACO2" "ENSA" "SEC13" "MYL6" "IMMT" "DTNBP1" "EIF4E"
[34] "COPS6" "NDUFV2" "WASHC5" "PLOD2" "PAICS" "NPEPPS" "ABLIM2" "PXN" "MTHFD1" "SEC23A" "MYG1"
[45] "LIMA1" "ARL1" "F8W809" "GPATCH4" "LIMS1" "G3V180" "APOO" "PIN4" "SQLE" "H3BNC9" "LMF1"
[56] "ACTN4" "TTC27" "NAA38" "TUBB6" "SARS2" "AP3B1" "PDHX" "PSMD14" "PPP6C" "TNPO2" "SCAMP3"
[67] "ERC2" "ARPC3" "CAPN5" "NDUFA2" "NDUFS5" "TIMM8A" "NRP2" "OGA" "RANBP6" "ROCK2" "CSDE1"
[78] "STAM2" "ATP5PD" "PLPBP" "LYPLA2" "IPO7" "ECEL1" "AIFM1" "SOD1" "PGK1" "GAPDH" "ATP1A1"
[89] "ALDH2" "ATP5F1B" "ENO1" "PFN1" "HSP90AB1" "ACAA1" "DLD" "ESD" "SLC2A3" "PC" "SLC25A6"
[100] "CKB" "RNH1" "TPT1" "ETFA" "AKR1A1" "GSPT1" "NCK1" "PSMC3" "VCL" "CALB2" "ACP1"
[111] "TARS1" "MAPK1" "ALDH4A1" "PRDX3" "ATP5F1D" "ADSS2" "SLC7A1" "HIBADH" "ATIC" "KIF5B" "AGL"
[122] "PSMC2" "PGM1" "GNL1" "ETFB" "RBMX" "HSPA9" "ADCY8" "EIF2S3" "USP5" "GNPDA1" "ATP5PO"
[133] "PREP" "IDH2" "TUFM" "AARS1" "HINT1" "EMD" "CCT4" "RAB7A" "ALDH3A2" "CAPZA1" "CRIP2"
[144] "SLC25A1" "ARFIP1" "SUCLG1" "PRKAG1" "CSE1L" "VCP" "ATP5MJ" "EEFSEC" "ARPC4" "DSTN" "RAB8A"
[155] "UBE2M" "SST" "RAN" "AP2B1" "PPP2R2A" "MRPS15" "TFAM" "GLO1" "AKR1C1" "SSBP1" "PTPN11"
[166] "APLP2" "C1QBP" "VAC14" "AIMP1" "TRAP1" "TRAF2" "PRKAA1" "MRPL49" "SLC39A6" "PICALM" "NAE1"
[177] "CTTN" "HES1" "KPNB1" "GAPVD1" "RAB3GAP1" "PEA15" "TAB1" "CDC37" "NDUFA5" "HADH" "LRRFIP1"
[188] "MTUS2" "ACADM" "SARS1" "NT5DC1" "ATG9B" "HIBCH" "PKN3" "SARM1" "VPS13C" "FASTKD5" "HUWE1"
[199] "STX12" "FUNDC1" "SULF1" "TPH2" "GSPT2" "LGI2" "NDUFAF2" "LRRC47" "ARMC10" "RDH11" "LARP4B"
[210] "GCN1" "MRPS31" "NECTIN2" "TFG" "STAM" "GCDH" "IGSF8" "PHYHIPL" "IPO9" "PSMB7" "APOL2"
[221] "CORO1B" "CNPY3" "MRPS26" "LNPK" "RTN4" "NANS" "OLA1" "ABHD10" "ETNK2" "CISD1" "LMCD1"
[232] "PTGFRN" "ANKFY1" "ATXN10" "COPG2" "STK39" "TRMT112" "VPS28" "DNAJC12" "ZMYND8" "DDX19B" "MAGED2"
[243] "PSMD13" "RUVBL2" "PLAA" "RUVBL1" "MRPS28" "MRPS17" "TMA7" "ACOT9" "LUC7L2" "STRAP" "PPME1"
[254] "CDC42BPB" "HEBP2" "PSAT1" length(overlap_genes)[1] 256write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/PRKN_DPE.csv")
print("saved")[1] "saved"contrast_list <- c("IGSF9B-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to IGSF9B")[1] "Unique to IGSF9B"print(overlap_genes) [1] "ESYT2" "ILVBL" "DENND3" "CASTOR2" "FAM171A2" "MCRIP1"
[7] "SLC35A4" "DNASE2" "KIF2A" "MYO1C" "DFFA" "MANBA"
[13] "RNASET2" "PODXL" "ARID1A" "SDHD" "COX7A2L" "UBFD1"
[19] "ABLIM1" "ADAM10" "EI24" "PDCD5" "TPP1" "CPLX1"
[25] "PSMA7" "TIMM23" "UQCRQ" "CLGN" "SPTBN2" "KIF3B"
[31] "SNPH" "DEGS1" "SCAMP2" "PGRMC2" "PFDN6" "RER1"
[37] "SPTLC1" "HMGB3" "STX7" "YKT6" "FABP7" "SEPTIN4"
[43] "SRGAP3" "TXNL1" "SYNJ1" "PROM1" "TSPAN6" "NDUFB5"
[49] "NDUFB3" "AP1G1" "SGTA" "SLC25A20" "NUDT21" "CALU"
[55] "EXTL3" "MFSD11" "KIF5C" "SELENOF" "TSPAN2" "TSPAN3"
[61] "DPM1" "TIMM17B" "CUTA" "PFDN1" "ABCB7" "SRGAP2"
[67] "ATP9A" "CLASP2" "PPFIA3" "TBCA" "MPDU1" "MACROH2A1"
[73] "PEX14" "NDUFB1" "ERLIN1" "HSBP1" "BANF1" "TIPRL"
[79] "PPM1B" "ADAP1" "ATP6AP2" "EIF3G" "EIF3J" "ATRN"
[85] "DGAT1" "ARL6IP5" "FLOT1" "TRIO" "RSL1D1" "B3GAT3"
[91] "DDHD2" "NFASC" "TMCC1" "ENDOD1" "WDR47" "YIF1A"
[97] "NDUFB4" "SBF1" "VAPB" "SNAPIN" "NDUFC2" "OXSR1"
[103] "GGPS1" "BAG2" "NDUFB10" "TOMM40" "PEX11B" "HPRT1"
[109] "ATP6" "IGF2" "APOE" "APOC3" "FTH1" "GBA1"
[115] "HLA-A" "RPN2" "SCG5" "ATP5MC1" "ITGB1" "GLA"
[121] "HEXA" "EPHX1" "LDHB" "PSAP" "LAMB1" "ENO2"
[127] "CLTA" "ANXA4" "COX6C" "SMIM13" "RAP2A" "TXN"
[133] "CTSA" "CHGA" "ACP2" "MAP2" "DBT" "RALB"
[139] "TOP2A" "UBL4A" "IGF2R" "COL6A1" "LAMP2" "PRKAR2A"
[145] "MIF" "UCHL3" "CD46" "ARSB" "RPA2" "COX7C"
[151] "H1-5" "H1-3" "H1-2" "GOT1" "NDUFB7" "GM2A"
[157] "SON" "GNAZ" "RAB3B" "GSTM3" "SYT1" "OSBP"
[163] "FBL" "FDXR" "GCSH" "CFL1" "EEF1B2" "GRK2"
[169] "PSMA1" "YWHAQ" "MARK3" "PSMB5" "TMOD1" "LAP3"
[175] "IMPA1" "GDI1" "TIA1" "UQCRC1" "STIP1" "S100A11"
[181] "GALNS" "MYH10" "ADD1" "BSG" "PPM1A" "HMGCL"
[187] "P36268" "DLST" "NUP62" "LIPA" "COL18A1" "MDH1"
[193] "HADHA" "CETN2" "CD200" "ACTR1B" "EPS15" "CASP3"
[199] "ECE1" "SSR1" "MAP1B" "RAP1GAP" "CAPZB" "TFPI2"
[205] "PIP4K2A" "CD151" "HSPA13" "AMPH" "INPP1" "HARS2"
[211] "PSEN1" "TSC2" "GSK3B" "NT5C2" "SEPHS1" "CPT1A"
[217] "ST13" "HCFC1" "ALDH5A1" "PSMD7" "KIF11" "MVD"
[223] "CTSC" "PTTG1IP" "SLC16A1" "RAD23A" "ALDH18A1" "MFAP2"
[229] "AFDN" "HNRNPH2" "BID" "ARPP19" "AP1S2" "CORO7"
[235] "EPPK1" "SEC61B" "S100A10" "UBE2N" "RPL27" "PCBD1"
[241] "SEC61A1" "DAD1" "PKIA" "SUMO2" "UFM1" "AP1S1"
[247] "YWHAG" "YWHAE" "ARF6" "RPS6" "RBX1" "RPL32"
[253] "PPIA" "VAMP2" "YWHAZ" "PPP2CA" "SEC11A" "UBE2L3"
[259] "TUBB4B" "GTF2I" "PIP4K2B" "SLC35A1" "ARG2" "MRPS11"
[265] "ARF5" "H3-3A" "H3-3B" "HSPG2" "SLC25A3" "CDK5"
[271] "TIAL1" "CTBS" "CAP1" "ATP2B2" "OCRL" "SLC25A11"
[277] "PTS" "KLC1" "ARHGAP1" "GOLGA3" "DMTN" "PPID"
[283] "SCRN3" "KIF1A" "SCRN1" "ANK3" "PAK1" "NME3"
[289] "MAD2L1" "UBE2V1" "PEDS1-UBE2V1" "DYNC1I2" "TMED1" "MTX1"
[295] "CAMK2B" "DCTN2" "STIM1" "ITGA7" "SPTAN1" "HNRNPD"
[301] "DAG1" "MLEC" "TTLL12" "MPP2" "DCTN1" "FLOT2"
[307] "INPP5A" "CLINT1" "LBR" "SPCS2" "EMC2" "PSMD6"
[313] "RRS1" "POSTN" "PDIA6" "PPP2R5B" "RCN1" "SF1"
[319] "MAPRE1" "RAB30" "ITSN1" "TBCE" "UBE2V2" "VAMP3"
[325] "DPYSL2" "CPSF6" "SMN2" "SMN1" "TST" "HAGH"
[331] "PTPRO" "HNRNPUL2" "CCDC88A" "LGALSL" "VPS26B" "CCDC184"
[337] "HSD17B12" "RAB6D" "SDK2" "TMEM97" "YIF1B" "MEST"
[343] "NOMO2" "WDR44" "SAMD4B" "CLVS2" "TPRG1L" "FNBP1L"
[349] "GPR158" "WLS" "Q5TF21" "MICOS10" "WASHC2A" "PPP2R2D"
[355] "MBLAC2" "LMBRD2" "IQSEC1" "CARMIL2" "RALGAPA1" "TCEAL6"
[361] "REEP3" "ARMC6" "JMJD6" "PPP1R18" "SLC48A1" "EDC4"
[367] "SCYL2" "CNNM4" "ERICH5" "MEAK7" "PGM2L1" "AAGAB"
[373] "TMEM65" "NCEH1" "CPLX2" "MOXD1" "CYP20A1" "POGLUT2"
[379] "TMEM205" "ISLR2" "APOOL" "PACS1" "RAB11FIP1" "HSDL2"
[385] "CRACD" "GPRIN3" "TOM1L2" "TMTC3" "IKBIP" "UBE2R2"
[391] "TLCD3B" "RUFY3" "EPM2AIP1" "TAOK1" "MOB1B" "CHMP1B"
[397] "MICAL3" "NUP54" "KIF21A" "TMED4" "GALNT7" "MAGI2"
[403] "USP48" "CAND1" "OSTM1" "DDX42" "NDUFA11" "DOLPP1"
[409] "GPSM1" "NUDCD3" "SLC44A2" "BRSK2" "SULF2" "SIRT2"
[415] "NRM" "SMAP1" "KIAA0319L" "NUP93" "ABHD12" "SLC43A2"
[421] "MMGT1" "CALHM5" "KCNRG" "JAGN1" "COMMD1" "EMC1"
[427] "AMER2" "GOLM1" "COLGALT1" "SUMF2" "TMEM87A" "TXNDC5"
[433] "NECAP1" "CAMKV" "PLA2G15" "Q8NCU8" "MROH1" "EHBP1"
[439] "MCU" "NUP37" "NLGN2" "CADM4" "FGFBP3" "UBA3"
[445] "C18orf32" "GPX8" "RAPGEF6" "C16orf78" "PDCD6IP" "PSPC1"
[451] "PPM1E" "JDP2" "LZIC" "DDX1" "H1-10" "NCSTN"
[457] "TM9SF4" "WASF1" "SLC9A6" "NDRG1" "NUP205" "TTC9"
[463] "SORL1" "GGH" "NRCAM" "KIFAP3" "PTPRN2" "USP9X"
[469] "BORCS5" "UBE2E3" "NCLN" "CCDC47" "TMEM230" "FUBP1"
[475] "VTI1A" "MCUR1" "TOMM6" "ARL8A" "CCDC127" "EFHD2"
[481] "PYCR2" "DCPS" "PPP1R14B" "ISOC1" "FOXRED1" "RAB39B"
[487] "ERLEC1" "DAZAP1" "SGTB" "CYFIP2" "CNRIP1" "LINGO1"
[493] "OTUB1" "CERS2" "SFRP2" "CDK5RAP3" "FAM210B" "PHACTR3"
[499] "PRRC1" "VSTM2L" "RUNDC3B" "NAP1L5" "AGAP3" "MIA2"
[505] "NEDD4L" "MAGI1" "PIGS" "YME1L1" "TBCB" "SEC62"
[511] "TSC22D3" "TTC1" "PHB2" "ATXN2" "SIGMAR1" "EBNA1BP2"
[517] "H2BC13" "ECSIT" "SYT3" "TXNDC17" "NUDT16L1" "SDF4"
[523] "ESYT1" "TMEM43" "LMF2" "TUBB2B" "TMEM109" "PBDC1"
[529] "PTDSS2" "TMED9" "ACAT2" "AP1M1" "RAB34" "SEMA4C"
[535] "YIPF3" "NDEL1" "ROGDI" "PITHD1" "MAP1LC3B" "UBA5"
[541] "KLC2" "HDHD2" "MAGT1" "LMAN2L" "SIL1" "SH3BGRL3"
[547] "SLC38A1" "DPAGT1" "POFUT1" "PIGU" "CLSTN2" "EPB41L1"
[553] "SMDT1" "PRR36" "REEP1" "SFXN1" "COG4" "NMNAT1"
[559] "TMEM165" "GLOD4" "PCDH9" "MCCC2" "APMAP" "NPHS2"
[565] "DYNLRB1" "EMC7" "PFDN4" "EIF2B3" "RAB6B" "TOMM22"
[571] "LRRC4B" "RBM12" "STAU2" "SHFL" "TMEM106B" "AGPAT5"
[577] "CYRIB" "EXOC1" "TMEM38B" "NDUFB11" "OCIAD1" "BABAM2"
[583] "DPP3" "SCN3A" "SCN3B" "TERF2IP" "UGGT1" "ERAP1"
[589] "CHMP5" "HACD3" "KCMF1" "TBC1D7" "TBC1D7-LOC100130357" "TOMM7"
[595] "CAMSAP3" "DPM3" "NCDN" "MRC2" "CPNE7" "EXTL2"
[601] "DNAJB11" "SEL1L" "PEF1" "ZMYM2" "SLC25A10" "CLIP2"
[607] "CFDP1" "SEC63" "CHORDC1" "UBQLN2" "PCYOX1" "PFDN2"
[613] "PUF60" "NAGK" "SH3BGRL2" "PURG" "GGA1" "JPT1"
[619] "TNIK" "PITPNC1" "MAN1B1" "CORO1C" "HPCAL4" "MYO6"
[625] "UBQLN1" "SYNRG" "SSR3" "FAF1" "MINPP1" "NOVA2"
[631] "NSFL1C" "MAPK8IP3" "USP24" "AGTPBP1" "MAPRE3" "CORO2B"
[637] "NUDC" "MAN2B2" "EFR3B" "DIS3" "GSTK1" "LAMTOR2"
[643] "CARHSP1" "SUGT1" "COQ6" "NSG2" "SH3GLB1" "DHRS7"
[649] "TMED5" "TMED7" "HDGFL3" "RABGAP1" "TMED3" "WNK2"
[655] "KIF3A" "USP15" "MYO5A" "RBM7" "TIMM22" "TIMM10B"
[661] "IER3IP1" "LRRFIP2" "SPIN1" "SPCS1" "NDUFB9" "FAM169A" length(overlap_genes)[1] 666write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_DPE.csv")
print("saved")[1] "saved"contrast_list <- c("INPP5F-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to INPP5F")[1] "Unique to INPP5F"print(overlap_genes) [1] "PIGBOS1" "CLIC1" "STX16" "U2SURP" "NACAD" "XPOT" "STX6" "SIN3B" "DAB1" "TUSC2" "ACTL6B" "ZRANB2" "BAG3" "CFB" "ASS1"
[16] "P01861" "APOH" "HPX" "S100B" "SNRNP70" "SLC2A1" "POLR2E" "AK4" "ERP29" "GCHFR" "SHMT2" "GARS1" "NSG1" "SLC1A4" "SUB1"
[31] "MANF" "TRA2B" "SRSF2" "PPARD" "FLII" "SRSF6" "SQSTM1" "PRP4K" "IDI1" "SAFB2" "SMC1A" "CHD4" "RNPS1" "TERF2" "PTPRN"
[46] "UBR4" "ARFGEF3" "ARSK" "ARMCX2" "NDUFAF7" "MICALL1" "PRUNE2" "COPS9" "KCTD12" "PPP1R9B" "CNN2" "SF3B5" "C1QTNF4" "SPRY4" "PNN"
[61] "GHITM" "EHMT1" "GMPR2" "TAGLN3" length(overlap_genes)[1] 64write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/INPP5F_DPE.csv")
print("saved")[1] "saved"contrast_list <- c("SH3GL2-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to SH3GL2")[1] "Unique to SH3GL2"print(overlap_genes) [1] "AIP" "GTPBP1" "MGRN1" "CNTN5" "KBTBD11" "POLR2C" "GLRX" "ALDH7A1" "KPNA1" "TPD52" "NCALD" "GRM7" "ETFDH"
[14] "FRY" "TBC1D9B" "ZC3HAV1" "ARHGAP12" "GDPD1" "NUDT10" "ITFG1" "MICAL1" "SH3KBP1" "CHAMP1" "LYSMD1" "TSPAN18" "OLFM1"
[27] "DNAJC5" "HEBP1" "ABCB8" "TMEM30A" "GDE1" "GPRC5B" "EHD3" "CACFD1" "SEPTIN9" "NFU1" "GAB2" length(overlap_genes)[1] 37write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SH3GL2_DPE.csv")
print("saved")[1] "saved"contrast_list <- c("IP6K2-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IP6K2")[1] "Overlap of IP6K2"print(overlap_genes) [1] "BRD4" "PRKCA" "PLCG1" "FDFT1" "MTIF2" "DLG4" "EPS8" "RAPGEF1" "FKBP8" "CYP51A1" "PRPF40B" "SCAI" "SYNE1" "FBXO41" "SPOCK2"
[16] "EVI5L" "FYTTD1" "CAMK2N2" "NT5DC2" "SERINC1" "RPRM" "TES" "OGDHL" "TDRKH" "TMX2" length(overlap_genes)[1] 25write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IP6K2_DPE.csv")
print("saved")[1] "saved"Make one dataframe for expression lists
# look at each abundance dataframe
colnames(df.bright) [1] "Symbol" "A53T.1" "A53T.2" "A53T.3" "Control.1" "Control.2" "Control.3" "GBA.KO.1" "GBA.KO.2" "GBA.KO.3" "PINK1.KO.1"
[12] "PINK1.KO.2" "PINK1.KO.3" "PRKN.KO.1" "PRKN.KO.2" "PRKN.KO.3" colnames(df.dark) [1] "Symbol" "Control.1" "Control.2" "Control.3" "Control.4" "IGSF9B.KO.1" "IGSF9B.KO.2" "INPP5F.KO.1" "INPP5F.KO.2" "INPP5F.KO.3"
[11] "IP6K2.KO.1" "IP6K2.KO.2" "IP6K2.KO.4" "SH3GL2.KO.1" "SH3GL2.KO.2" "SH3GL2.KO.3"Try some plots
df.long <- protein_zscore(data = df,
proteins = lysome$`Gene name`, # Example protein names
sample_patterns = c("Control","A53T","GBA.KO","PINK1.KO","PRKN.KO","INPP5F.KO","IGSF9B.KO" ,"SH3GL2.KO","IP6K2.KO"), group_means = TRUE) [1] "Sample columns selected: A53T.1, A53T.2, A53T.3, Control.1.x, Control.2.x, Control.3.x, GBA.KO.1, GBA.KO.2, GBA.KO.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3, PRKN.KO.1, PRKN.KO.2, PRKN.KO.3, Control.1.y, Control.2.y, Control.3.y, Control.4, IGSF9B.KO.1, IGSF9B.KO.2, INPP5F.KO.1, INPP5F.KO.2, INPP5F.KO.3, IP6K2.KO.1, IP6K2.KO.2, IP6K2.KO.4, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
max(df.long$Abundance, na.rm = TRUE)[1] 4.035141min(df.long$Abundance, na.rm = TRUE)[1] -1.118153# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
data = df,
proteins = lysome$`Gene name`, # Example protein names
sample_patterns = c("Control", "A53T","GBA.KO","PINK1.KO","PRKN.KO","INPP5F.KO","IGSF9B.KO", "SH3GL2.KO","IP6K2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1.5,-1,-0.5, 0, 2,4.5), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
cell_width = 20, # Control column width
cell_height = 10 # Control row height
)[1] "Sample columns selected: A53T.1, A53T.2, A53T.3, Control.1.x, Control.2.x, Control.3.x, GBA.KO.1, GBA.KO.2, GBA.KO.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3, PRKN.KO.1, PRKN.KO.2, PRKN.KO.3, Control.1.y, Control.2.y, Control.3.y, Control.4, IGSF9B.KO.1, IGSF9B.KO.2, INPP5F.KO.1, INPP5F.KO.2, INPP5F.KO.3, IP6K2.KO.1, IP6K2.KO.2, IP6K2.KO.4, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
data = df,
proteins = lysome$`Gene name`, # Example protein names
sample_patterns = c("Control.", "A53T.","GBA.KO","PINK1.KO","PRKN.KO","INPP5F.KO","IGSF9B.KO", "SH3GL2.KO","IP6K2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1,-0.5, 0, 2.5,5), # Adjust based on your data range
group_means = FALSE, # Set to FALSE if you want individual samples
cell_width = 20, # Control column width
cell_height = 10 # Control row height
)[1] "Sample columns selected: A53T.1, A53T.2, A53T.3, Control.1.x, Control.2.x, Control.3.x, GBA.KO.1, GBA.KO.2, GBA.KO.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3, PRKN.KO.1, PRKN.KO.2, PRKN.KO.3, Control.1.y, Control.2.y, Control.3.y, Control.4, IGSF9B.KO.1, IGSF9B.KO.2, INPP5F.KO.1, INPP5F.KO.2, INPP5F.KO.3, IP6K2.KO.1, IP6K2.KO.2, IP6K2.KO.4, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
Heat map of Logfold change
Same function but controling keeping the gene list order
Plot by dendrogram
# now plotted with function below
gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1)
print(p1)
gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
p2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1)
print(p2)
gene_list <- lysome$`Gene name`
contrast_list <- c("IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p3 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1)
print(p3)
gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1)
print(p4)
NA
NA
gene_list <- lysome$`Gene name`
contrast_list1 <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
contrast_list2 <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
contrast_list3 <- c("IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
contrast_list4 <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
pdf("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/HM_lysomeList_Log2FC_dendrogram.pdf", width = 6, height = 8)
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list1, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Lysosome Genes")
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list1, remove_na_genes = TRUE, column_width = 1,title = "Log Fold Change Lysosome Genes")plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list3, remove_na_genes = TRUE, column_width = 1,title = "Log Fold Change Lysosome Genes")
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list4, remove_na_genes = TRUE, column_width = 1,title = "Log Fold Change Lysosome Genes")dev.off()null device
1 get another gene list from Roxanne’s list
length(mito.genes)
[1] 1136
gene_list <- mito.genes[1:20]
#gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 12)Warning: Values from `log2_ratio` are not uniquely identified; output will contain list-cols.
• Use `values_fn = list` to suppress this warning.
• Use `values_fn = {summary_fun}` to summarise duplicates.
• Use the following dplyr code to identify duplicates.
{data} |>
dplyr::summarise(n = dplyr::n(), .by = c(Symbol, Contrast)) |>
dplyr::filter(n > 1L)Error in `dplyr::mutate()`:
ℹ In argument: `across(-Symbol, as.numeric)`.
Caused by error in `across()`:
! Can't compute column `SNCA-A53T`.
Caused by error:
! 'list' object cannot be coerced to type 'double'
Backtrace:
1. global plot_logfold_change_heatmap_dendrogram(...)
4. dplyr:::mutate.data.frame(., across(-Symbol, as.numeric))
5. dplyr:::mutate_cols(.data, dplyr_quosures(...), by)
7. dplyr:::mutate_col(dots[[i]], data, mask, new_columns)
9. mask$eval_all_mutate(quo)
10. dplyr (local) eval()New function to skip genes not found in df_list
print(mitocarta$Symbol[11:20])
[1] "COX5A" "ISCA2" "PMPCB" "UQCRFS1" "ATP5F1A" "OGDH" "PDHB" "UQCRC2" "SDHD" "MRPS35" This error seems to mean there are duplicate enteries for the same gene
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol[1:10], contrast_list,
remove_na_genes = TRUE, column_width = 1)
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol[10:20], contrast_list,
remove_na_genes = TRUE, column_width = 1)
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol[11:60], contrast_list,
remove_na_genes = TRUE, column_width = 0.8)
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol[1:20], contrast_list,
remove_na_genes = TRUE, column_width = 1)Error in hclust(d, method = method) :
NA/NaN/Inf in foreign function call (arg 10)We now need to remove problematic values
Force 0 to be the center of the scale
plot_logfold_change_heatmap_dendrogram <- function(df_list, gene_list, contrast_list,
remove_na_genes = FALSE, column_width = 0.8, title = "Log Fold Change") {
# Filter data to only include the specified genes and contrasts
filtered_data <- df_list[contrast_list] %>%
lapply(function(df) {
df %>%
dplyr::filter(Symbol %in% gene_list) %>%
dplyr::select(Symbol, log2_ratio)
}) %>%
dplyr::bind_rows(.id = "Contrast")
# Ensure only genes in gene_list that are found in the filtered data are kept
filtered_data <- filtered_data %>%
dplyr::filter(Symbol %in% unique(filtered_data$Symbol))
# Handle duplicates by summarizing (e.g., taking the mean)
filtered_data <- filtered_data %>%
dplyr::group_by(Symbol, Contrast) %>%
dplyr::summarize(log2_ratio = mean(log2_ratio, na.rm = TRUE), .groups = 'drop')
# Create a wide format matrix for heatmap plotting
data_wide <- filtered_data %>%
tidyr::pivot_wider(names_from = Contrast, values_from = log2_ratio) %>%
dplyr::filter(Symbol %in% gene_list) %>% # Ensure only genes in gene_list are kept
dplyr::mutate(dplyr::across(-Symbol, as.numeric))
# Optionally remove genes that are NA across all contrasts
if (remove_na_genes) {
data_wide <- data_wide %>%
dplyr::filter(rowSums(is.na(dplyr::select(., -Symbol))) < ncol(data_wide) - 1)
}
# Create a matrix for heatmap plotting
mat <- as.matrix(data_wide %>% dplyr::select(-Symbol))
rownames(mat) <- data_wide$Symbol
# Check for NA, NaN, or Inf values in the matrix and remove any rows or columns that contain them
mat <- mat[complete.cases(mat), ] # Remove rows with NA/NaN/Inf values
mat <- mat[, colSums(is.na(mat)) == 0] # Remove columns with NA/NaN/Inf values
# If after removing NA rows/columns the matrix becomes empty, return an informative error
if (nrow(mat) == 0 || ncol(mat) == 0) {
stop("The matrix is empty after removing rows/columns with NA/NaN/Inf values. No valid data to plot.")
}
# Determine the limits for the scale to be symmetric around zero
max_val <- max(abs(mat), na.rm = TRUE)
# Create the heatmap with dendrograms
pheatmap::pheatmap(mat,
cluster_rows = TRUE,
cluster_cols = TRUE,
scale = "none",
color = colorRampPalette(c("blue", "white", "red"))(50),
breaks = seq(-max_val, max_val, length.out = 51), # Symmetric color scale
cellwidth = column_width * 10, # Adjust column width
show_rownames = TRUE,
show_colnames = TRUE,
main = title)
}
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol, contrast_list,
remove_na_genes = TRUE, column_width = 1)
NA
NA
NAGene different heatmaps
gene_list <- mito.genes
#gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes")
print(p1)
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
p2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes")
print(p2)
contrast_list <- c("IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p3 <-plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes")
print(p3)
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes")
print(p4)
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[1:100]
p1.1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 1-100")
p1.1 
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[101:200]
p1.2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 101-200")
p1.2
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[201:300]
p1.3 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 201-300")
p1.3
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[301:400]
p1.4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 301-400")
p1.4
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[401:600]
p1.5 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 401-600")
p1.5
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[601:800]
p1.6 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 601-800")
p1.6
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[801:1136]
p1.7 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 801-1136")
p1.7
NA
NA
NASave mitocharta plots
pdf("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/HM_Mitocharta_Log2FC_dendrogram.pdf")
p1
p1.1
p1.2
p1.3
p1.4
p1.5
p1.6
p1.7
p2
p3
p4
dev.off()null device
1 Read in another list of genes
head(pd.genes.list)
pd.genes <- pd.genes.list$SymbolLook at the gene list for PD
gene_list <- pd.genes
#gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes")
print(p1)
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
p2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes")
print(p2)
contrast_list <- c("IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p3 <-plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes")
print(p3)
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes")
print(p4)
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[1:50]
p1.1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 1-50")
p1.1 
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[50:100]
p1.2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 50-100")
p1.2
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[100:150]
p1.3 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 100-150")
p1.3
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[150:200]
p1.4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 150-200")
p1.4
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[200:250]
p1.5 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 200-250")
p1.5
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[250:300]
p1.6 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 250-300")
p1.6
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[300:330]
p1.7 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 300-330")
p1.7
NA
NApdf("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/HM_PD_genes_Log2FC_dendrogram.pdf")
p1
p1.1
p1.2
p1.3
p1.4
p1.5
p1.6
p1.7
p2
p3
p4
dev.off()null device
1 Read synapse list
synapse.genes.list <- read_excel("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/List of genes for RNAseq or Proteomics/SYNAPSE_REDUCED_GENE_LIST.xlsx", col_names = FALSE)New names:
• `` -> `...1`head(synapse.genes.list)colnames(synapse.genes.list) <- c("Symbol")
head(synapse.genes.list)synapse.genes <- synapse.genes.list$Symbolgene_list <- synapse.genes
#gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes")
print(p1)
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
p2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes")
print(p2)
contrast_list <- c("IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p3 <-plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes")
print(p3)
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes")
print(p4)
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- synapse.genes[1:100]
p1.1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes 1-100")
p1.1 
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- synapse.genes[100:200]
p1.2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes 100-200")
p1.2
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- synapse.genes[200:300]
p1.3 <-plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes 200-300")
p1.3
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- synapse.genes[300:400]
p1.4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes 300-400")
p1.4
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- synapse.genes[400:500]
p1.5 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes 400-500")
p1.5
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- synapse.genes[500:594]
p1.6 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes 500-594")
p1.6
save the synaptic gene list
pdf("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/HM_Synpatic_genes_Log2FC_dendrogram.pdf")
p1
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p1.2
p1.3
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p4
dev.off()null device
1 GWAS PD
gwas.genes <- read.csv("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/List of genes for RNAseq or Proteomics/ALL_PD_GWAS_GENELIST.csv")
head(gwas.genes)All gwas genes
print(gene_list)
[1] "ASXL3" "BAG3" "BIN3" "BRIP1" "BST1" "C5orf24" "CAB39L" "CAMK2D" "CASC16" "CD19"
[11] "CHRNB1" "CLCN3" "CRHR1" "CRLS1" "CTSB" "DDX46" "DGKQ" "DLG2" "DLST" "DNAH17"
[21] "DYRK1A" "EHMT2" "ELOVL7" "FAM171A2" "FAM47E" "FAM47E-STBD1" "FAM49B" "FBRSL1" "FCGR2A" "FGF11"
[31] "GAK" "GALC" "GBA" "GBAP1" "GBF1" "GCH1" "GPNMB" "GPR65" "GRN" "HIP1R"
[41] "IGSF9B" "INPP5F" "IP6K2" "ITGA8" "ITPKB" "KANSL1" "KCNIP3" "KCNS3" "KPNA1" "KRTCAP2"
[51] "LCORL" "LINC00693" "LRRK2" "MAP4K4" "MAPT-AS1" "MBNL2" "MCCC1" "MED12L" "MIPOL1" "MIR4308"
[61] "MRVI1-AS1" "MUC19" "NOD2" "NSF" "NUCKS1" "P2RY12" "PAM" "PGS1" "PMVK" "RAB7L1"
[71] "RABEP2" "RIT2" "RNF141" "RPS6KL1" "SCARB2" "SEMA4A" "SETD1A" "SH3GL2" "SIPA1L2" "SLC25A44"
[81] "SLC44A4" "SNCA" "SPPL2B" "SPPL2C" "SPTSSB" "STK39" "SYT17" "TBC1D5" "TMEM163" "TMEM175"
[91] "TRIM40" "UBAP2" "UBTF" "VAMP4" "WNT3" "ZBTB4" p1.4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = FALSE, column_width = 1, title = "Log Fold Change GWAS Genes with distance < 10000")
p1.4
pdf("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/HM_GWAS_genes_Log2FC_dendrogram.pdf")
p1
p1.1
p1.2
p1.3
p1.4
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p4
dev.off()
---
title: "R Notebook"
output: html_notebook
---


Differential analysis


# Protein
Read in all the DPE files calculated by JF Trempe Lab or/and  TMT-analyst by RL
Files separated by genotype/iPSC line in the workbook "ProcessFilesRenameAccession.Rmd"
All data is from 6 weeks DANs from iPSC lines in AIW002-02 background
Bright genome and dark genome where run separately

```{r}
# read in csv into to make a list of dataframes

# Load required library
library(readr)

# the protein DE files are here
folder_path <- "/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/DPE_files/"

# List all CSV files in the folder
csv_files <- list.files(path = folder_path, pattern = "\\.csv$", full.names = TRUE)

# Read all CSV files into a list of dataframes, skipping the first column
df_list <- lapply(csv_files, function(file) {
  read_csv(file, col_types = cols(.default = "?", `...1` = col_skip()))
})

# Optionally, name each element of the list with the respective file names (without the .csv extension)
names(df_list) <- tools::file_path_sans_ext(basename(csv_files))

# Print the names of the dataframes
print(names(df_list))

# test that these are dataframes

df.gba <- df_list$`GBA-KO_ProtomicsDifferentialAbundance`
head(df.gba)


```
Rename the list
```{r}
names(df_list) <- c("GBA-KO","IGSF9B-KO","INPP5F-KO","IP6K2-KO","PINK1-KO","PRKN-KO","SH3GL2-KO","SNCA-A53T")

print(names(df_list))
head(df_list$`GBA-KO`)

```

# Volcano plots
Thresholds: Log2 abundance ratio > 0.5 and p value < 0.05

```{r, fig.height=4}
#library(EnhancedVolcano)


pA53T <- EnhancedVolcano(df_list$`SNCA-A53T`,
    lab = df_list$`SNCA-A53T`$Symbol,
    x = 'log2_ratio',
    y = 'p-value',
    pCutoff = 0.05,
    FCcutoff = 0.5,
    colAlpha = 0.5,
    labSize = 5,
    xlim = c(-3,3),
    ylim = c(0, 7),
    drawConnectors = FALSE,
    widthConnectors = 0.1,
    max.overlaps = 40,
    legendPosition = "right",
    title = "SNCA-A53T vs Control",
    subtitle = "Differential Protein Abundance"
   )  + scale_x_continuous(breaks = seq(-3, 3, by = 0.5)) + # Adjust the x-axis breaks as needed
scale_y_continuous(limits = c(0, 7), expand = c(0, 0)) +  # Remove space below the y-axis
  coord_cartesian(xlim = c(-3, 3), ylim = c(0, 7)) +  # Ensure that the plot does not display points beyond this range
 theme(axis.text.x = element_text(size = 14), axis.text.y = element_text(size = 14))  # Adjust x-axis label size



pGBA <- EnhancedVolcano(df_list$`GBA-KO`,
    lab = df_list$`GBA-KO`$Symbol,
    x = 'log2_ratio',
    y = 'p-value',
    pCutoff = 0.05,
    FCcutoff = 0.5,
    colAlpha = 0.5,
    labSize = 5,
    xlim = c(-3,3),
    ylim = c(0, 7),
    drawConnectors = FALSE,
    widthConnectors = 0.1,
    max.overlaps = 40,
    legendPosition = "right",
    title = "GBA-KO vs Control",
    subtitle = "Differential Protein Abundance"
   )  + scale_x_continuous(breaks = seq(-4, 4, by = 0.5)) + # Adjust the x-axis breaks as needed
scale_y_continuous(limits = c(0, 7), expand = c(0, 0)) +  # Remove space below the y-axis
  coord_cartesian(xlim = c(-3, 3), ylim = c(0, 7)) +  # Ensure that the plot does not display points beyond this range
 theme(axis.text.x = element_text(size = 14), axis.text.y = element_text(size = 14))  # Adjust x-axis label size



pPINK1 <- EnhancedVolcano(df_list$`PINK1-KO`,
    lab = df_list$`PINK1-KO`$Symbol,
    x = 'log2_ratio',
    y = 'p-value',
    pCutoff = 0.05,
    FCcutoff = 0.5,
    colAlpha = 0.5,
    labSize = 5,
    xlim = c(-4,3),
    ylim = c(0, 7),
    drawConnectors = FALSE,
    widthConnectors = 0.1,
    max.overlaps = 40,
    legendPosition = "right",
    title = "PINK1-KO vs Control",
    subtitle = "Differential Protein Abundance"
   ) + scale_x_continuous(breaks = seq(-4, 4, by = 0.5)) +  # Adjust the x-axis breaks as needed
  scale_y_continuous(limits = c(0, 7), expand = c(0, 0)) +  # Remove space below the y-axis
  coord_cartesian(xlim = c(-4, 3), ylim = c(0, 7)) + # Ensure that the plot does not display points beyond this range
 theme(axis.text.x = element_text(size = 14), axis.text.y = element_text(size = 14))  # Adjust x-axis label size

# Plot for PRKN-KO
pPRKN <- EnhancedVolcano(df_list$`PRKN-KO`,
    lab = df_list$`PRKN-KO`$Symbol,
    x = 'log2_ratio',
    y = 'p-value',
    pCutoff = 0.05,
    FCcutoff = 0.5,
    colAlpha = 0.5,
    labSize = 5,
    xlim = c(-5,4),
    ylim = c(0, 10.5),
    drawConnectors = FALSE,
    widthConnectors = 0.1,
    max.overlaps = 40,
    legendPosition = "right",
    title = "PRKN-KO vs Control",
    subtitle = "Differential Protein Abundance"
   ) + scale_x_continuous(breaks = seq(-5, 4, by = 0.5)) +  # Adjust the x-axis breaks as needed
  scale_y_continuous(limits = c(0, 10.5), expand = c(0, 0)) +  # Remove space below the y-axis
  coord_cartesian(xlim = c(-5, 4), ylim = c(0, 10.5)) +  # Ensure that the plot does not display points beyond this range
  theme(axis.text.x = element_text(size = 14), axis.text.y = element_text(size = 14))  # Adjust x-axis label size

pIGSF9B <- EnhancedVolcano(df_list$`IGSF9B-KO`,
    lab = df_list$`IGSF9B-KO`$Symbol,
    x = 'log2_ratio',
    y = 'p-value',
    pCutoff = 0.05,
    FCcutoff = 0.5,
    colAlpha = 0.5,
    labSize = 5,
    xlim = c(-4,5.5),
    ylim = c(0, 11),
    drawConnectors = FALSE,
    widthConnectors = 0.1,
    max.overlaps = 40,
    legendPosition = "right",
    title = "IGSF9B-KO vs Control",
    subtitle = "Differential Protein Abundance"
   ) + scale_x_continuous(breaks = seq(-4, 5.5, by = 0.5)) +  # Adjust the x-axis breaks as needed
  scale_y_continuous(limits = c(0, 11), expand = c(0, 0)) +  # Remove space below the y-axis
  coord_cartesian(xlim = c(-4, 5.5), ylim = c(0, 11)) +  # Ensure that the plot does not display points beyond this range
  theme(axis.text.x = element_text(size = 14), axis.text.y = element_text(size = 14))  # Adjust x-axis label size



pINPP5F <- EnhancedVolcano(df_list$`INPP5F-KO`,
    lab = df_list$`INPP5F-KO`$Symbol,
    x = 'log2_ratio',
    y = 'p-value',
    pCutoff = 0.05,
    FCcutoff = 0.5,
    colAlpha = 0.5,
    labSize = 5,
    xlim = c(-3,3),
    ylim = c(0, 10.5),
    drawConnectors = FALSE,
    widthConnectors = 0.1,
    max.overlaps = 40,
    legendPosition = "right",
    title = "INPP5F-KO vs Control",
    subtitle = "Differential Protein Abundance"
   ) + scale_x_continuous(breaks = seq(-3, 3, by = 0.5)) +  # Adjust the x-axis breaks as needed
  scale_y_continuous(limits = c(0, 10.5), expand = c(0, 0)) +  # Remove space below the y-axis
  coord_cartesian(xlim = c(-3, 3), ylim = c(0, 10.5)) +  # Ensure that the plot does not display points beyond this range
  theme(axis.text.x = element_text(size = 14), axis.text.y = element_text(size = 14))  # Adjust x-axis label size


pIP6K2 <- EnhancedVolcano(df_list$`IP6K2-KO`,
    lab = df_list$`IP6K2-KO`$Symbol,
    x = 'log2_ratio',
    y = 'p-value',
    pCutoff = 0.05,
    FCcutoff = 0.5,
    colAlpha = 0.5,
    labSize = 5,
    xlim = c(-2,5.2),
    ylim = c(0, 8),
    drawConnectors = FALSE,
    widthConnectors = 0.1,
    max.overlaps = 40,
    legendPosition = "right",
    title = "IP6K2-KO vs Control",
    subtitle = "Differential Protein Abundance"
   ) + scale_x_continuous(breaks = seq(-2.5, 2, by = 0.5)) +  # Adjust the x-axis breaks as needed
  scale_y_continuous(limits = c(0, 8), expand = c(0, 0)) +  # Remove space below the y-axis
  coord_cartesian(xlim = c(-2.5, 2), ylim = c(0, 8)) +  # Ensure that the plot does not display points beyond this range
  theme(axis.text.x = element_text(size = 14), axis.text.y = element_text(size = 14))  # Adjust x-axis label size


pSH3GL2 <- EnhancedVolcano(df_list$`SH3GL2-KO`,
    lab = df_list$`SH3GL2-KO`$Symbol,
    x = 'log2_ratio',
    y = 'p-value',
    pCutoff = 0.05,
    FCcutoff = 0.5,
    colAlpha = 0.5,
    labSize = 5,
    xlim = c(-3,2.5),
    ylim = c(0, 10.5),
    drawConnectors = FALSE,
    widthConnectors = 0.1,
    max.overlaps = 40,
    legendPosition = "right",
    title = "SH3GL2-KO vs Control",
    subtitle = "Differential Protein Abundance"
   ) + scale_x_continuous(breaks = seq(-3, 2.5, by = 0.5)) +  # Adjust the x-axis breaks as needed
  scale_y_continuous(limits = c(0, 10.5), expand = c(0, 0)) +  # Remove space below the y-axis
  coord_cartesian(xlim = c(-3, 2.5), ylim = c(0, 10.5)) +  # Ensure that the plot does not display points beyond this range
  theme(axis.text.x = element_text(size = 14), axis.text.y = element_text(size = 14))  # Adjust x-axis label size




pA53T
pGBA
pPINK1
pPRKN
pIGSF9B
pINPP5F
pIP6K2
pSH3GL2

```

Make a filtered list of dataframes with the thresholds

```{r}
colnames(df_list$`GBA-KO`)
```



```{r}
# use function to filter the list of dataframes
# update function to match column names

# Function to filter DGE results based on criteria signle DGE dataframe filter function
filter_dge_results <- function(dge_results, logFC_threshold = NULL, logFC_direction = NULL, p_threshold = 0.01, p_col = "p-value") {
  if (!is.null(logFC_threshold)) {
    if (logFC_direction == "positive") {
      dge_results <- dge_results %>%
        filter(log2_ratio > logFC_threshold)
    } else if (logFC_direction == "negative") {
      dge_results <- dge_results %>%
        filter(log2_ratio < -logFC_threshold)
    } else if (logFC_direction == "both") {
      dge_results <- dge_results %>%
        filter(abs(log2_ratio) > logFC_threshold)
    }
  }
  
  dge_results_filtered <- dge_results %>%
    filter(!!sym(p_col) < p_threshold)
  
  return(dge_results_filtered)
}

# Apply filtering to DGE lists - uses the function above to filter each cell type list inside each contrast list
filter_dge_lists <- function(dge_lists, logFC_threshold = 0.25, logFC_direction = "both", p_threshold = 0.01, p_col = "p_val_adj") {
  dge_lists_filtered <- lapply(dge_lists, function(dge_list) {
    lapply(dge_list, function(dge_df) {
      filtered_df <- filter_dge_results(dge_df, logFC_threshold, logFC_direction, p_threshold, p_col)
      return(filtered_df)
    })
  })
  return(dge_lists_filtered)
}




# Function to convert specified columns to numeric
convert_to_numeric <- function(df, cols) {
  df[cols] <- lapply(df[cols], as.numeric)
  return(df)
}

# Apply conversion to each dataframe in the list
convert_list_columns <- function(df_list, cols) {
  lapply(df_list, function(df) {
    convert_to_numeric(df, cols)
  })
}

# Columns to convert
numeric_cols <- c("p-value", "log2_ratio")

# Convert columns to numeric in all dataframes in the list
df_list_numeric <- convert_list_columns(df_list, numeric_cols)

# For filtering lists of DGE results
filtered_DEP <- filter_dge_lists(df_list_numeric, logFC_threshold = 0.5, logFC_direction = "both", p_threshold = 0.05, p_col = "p-value")


head(df_list$`IGSF9B-KO`)
head(df_list_numeric$`IGSF9B-KO`)

```

```{r}
library(dplyr)

# Function to filter DGE results based on criteria
filter_dge_results <- function(dge_results, logFC_threshold = NULL, logFC_direction = NULL, p_threshold = 0.01, p_col = "p-value") {
  # Debugging: Print column names to check if p_col is present
  print(paste("Columns in dataframe:", paste(colnames(dge_results), collapse = ", ")))
  
  if (!p_col %in% colnames(dge_results)) {
    stop(paste("Column", p_col, "not found in the dataframe"))
  }
  
  # Ensure columns are numeric
  print(paste("Classes of columns:", paste(sapply(dge_results, class), collapse = ", ")))
  
  # Apply logFC filtering if specified
  if (!is.null(logFC_threshold)) {
    if (logFC_direction == "positive") {
      dge_results <- dge_results %>%
        filter(log2_ratio > logFC_threshold)
    } else if (logFC_direction == "negative") {
      dge_results <- dge_results %>%
        filter(log2_ratio < -logFC_threshold)
    } else if (logFC_direction == "both") {
      dge_results <- dge_results %>%
        filter(abs(log2_ratio) > logFC_threshold)
    }
  }
  
  # Apply p-value filtering
  dge_results_filtered <- dge_results %>%
    filter(!!sym(p_col) < p_threshold)
  
  return(dge_results_filtered)
}


test <- filter_dge_results(df_list$`GBA-KO`,logFC_direction = "both", logFC_threshold = 0.5, p_threshold = 0.05, p_col = "p-value")

# now apply the filter function across all df in one list

filter_dge_lists <- function(dge_lists, logFC_threshold = 0.25, logFC_direction = "both", p_threshold = 0.01, p_col = "p-value") {
  # Iterate over each dataframe in the list
  dge_lists_filtered <- lapply(dge_lists, function(dge_df) {
    # Debugging: Print column names of the dataframe being processed
    print(paste("Processing dataframe with columns:", paste(colnames(dge_df), collapse = ", ")))
    
    # Apply the filter function to each dataframe
    filtered_df <- filter_dge_results(dge_df, logFC_threshold, logFC_direction, p_threshold, p_col)
    
    return(filtered_df)
  })
  
  return(dge_lists_filtered)
}

# run the filtering
filtered_DEP <- filter_dge_lists(df_list, logFC_threshold = 0.5, logFC_direction = "both", p_threshold = 0.05, p_col = "p-value")



```

Get gene counts

```{r}

# function to count up and down regulated Proteins
count_regulations <- function(dge_df) {
  # Ensure columns are numeric
  dge_df$log2_ratio <- as.numeric(dge_df$log2_ratio)
  
  # Count upregulated and downregulated proteins
  upregulated_count <- sum(dge_df$log2_ratio > 0, na.rm = TRUE)
  downregulated_count <- sum(dge_df$log2_ratio < 0, na.rm = TRUE)
  
  return(c(Upregulated = upregulated_count, Downregulated = downregulated_count))
}

summarize_regulations <- function(dge_lists) {
  # Get names of the dataframes
  df_names <- names(dge_lists)
  
  # Apply the counting function to each dataframe and name the result
  counts_list <- lapply(dge_lists, function(df) {
    counts <- count_regulations(df)
    return(counts)
  })
  
  # Convert the list of counts into a dataframe
  result_df <- do.call(rbind, counts_list)
  
  # Set the row names to the names of the original dataframes
  rownames(result_df) <- df_names
  
  return(result_df)
}

# apply to filtered list
regulation_summary <- summarize_regulations(filtered_DEP)
print(regulation_summary)



```



```{r}
colnames(filtered_DEP$`PINK1-KO`)

```

Function to get the top n genes up and down

```{r}
# Function to select top n up and down regulated genes
select_top_genes <- function(dge_df, logFC_col = "log2_ratio", symbol_col = "Symbol", n = 10) {
  # Ensure log2_ratio column is numeric
  dge_df[[logFC_col]] <- as.numeric(dge_df[[logFC_col]])
  
  # Sort dataframe by log2_ratio to get top upregulated and downregulated genes
  top_upregulated <- dge_df %>%
    arrange(desc(!!sym(logFC_col))) %>%
    head(n) %>%
    pull(!!sym(symbol_col))
  
  top_downregulated <- dge_df %>%
    arrange(!!sym(logFC_col)) %>%
    head(n) %>%
    pull(!!sym(symbol_col))
  
  # Combine upregulated and downregulated genes into a single vector
  top_genes <- c(top_upregulated, top_downregulated)
  
  return(top_genes)
}

# Example usage
top_genes <- select_top_genes(filtered_DEP$`PINK1-KO`, n = 10)
print(top_genes)



```

```{r}
# Function to select top n up and down regulated genes
select_top_genes <- function(dge_df, logFC_col = "log2_ratio", symbol_col = "Symbol", n = 10) {
  # Ensure log2_ratio column is numeric
  dge_df[[logFC_col]] <- as.numeric(dge_df[[logFC_col]])
  
  # Sort dataframe by log2_ratio to get top upregulated genes
  top_upregulated <- dge_df %>%
    arrange(desc(!!sym(logFC_col))) %>%
    head(n) %>%
    pull(!!sym(symbol_col))
  
  # Sort dataframe by log2_ratio to get top downregulated genes
  top_downregulated <- dge_df %>%
    arrange(!!sym(logFC_col)) %>%
    head(n) %>%
    pull(!!sym(symbol_col))
  
  # Combine upregulated and downregulated genes into a single vector
  # Upregulated first, followed by downregulated
  top_genes <- c(top_upregulated, top_downregulated)
  
  return(top_genes)
}

# Example usage
top_genes <- select_top_genes(filtered_DEP$`PINK1-KO`, n = 5)
print(top_genes)

```
```{r}

df.pink1 <- filtered_DEP$`PINK1-KO`


```





Plot a heatmap of the top up and down genes


```{r}

library(ggplot2)
library(dplyr)
library(tidyr)

# Function to plot heatmap of relative abundance
plot_protein_heatmap <- function(data, proteins, sample_patterns, na_color = "grey") {
  # Filter the data for selected proteins
  data_filtered <- data %>%
    filter(Symbol %in% proteins)

  # Identify sample columns matching patterns
  sample_columns <- colnames(data_filtered)[sapply(colnames(data_filtered), function(col_name) {
    any(sapply(sample_patterns, function(p) grepl(p, col_name)))
  })]

  # Debug: Check contents of sample_columns
  print(paste("Sample columns selected:", paste(sample_columns, collapse = ", ")))

  # Check if sample_columns has valid entries
  if (length(sample_columns) == 0) {
    stop("No sample columns matched the patterns provided.")
  }

  # Select only columns matching sample patterns and the Symbol column
  data_filtered <- data_filtered %>%
    dplyr::select(Symbol, all_of(sample_columns))  # Explicitly use dplyr::select

  # Remove rows where all values are NA (excluding the Symbol column)
  data_filtered <- data_filtered %>%
    filter(rowSums(is.na(dplyr::select(., -Symbol))) < length(sample_columns))

  # Reshape data for ggplot
  data_long <- data_filtered %>%
    pivot_longer(
      cols = -Symbol, 
      names_to = "Sample", 
      values_to = "Abundance"
    )

  # Create the heatmap
  heatmap_plot <- ggplot(data_long, aes(x = Sample, y = Symbol, fill = Abundance)) +
    geom_tile(color = "white") +
    scale_fill_gradient2(low = "blue", mid = na_color, high = "red", na.value = na_color, midpoint = median(data_long$Abundance, na.rm = TRUE)) +
    theme_minimal() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
    labs(title = "Protein Abundance Heatmap", x = "Samples", y = "Proteins")
  
  return(heatmap_plot)
}




```



Plot control and IPSC line for each list 

```{r}

abundance.bright <- read.csv("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/ProtomicsNormGeneAbundance_bright.csv")

colnames(abundance.bright)

df <- abundance.bright[, c(3,5:16,18:20)]

df.bright <- abundance.bright[, c(3,5:16,18:20)]
# Example usage
# Assuming 'df' is your dataframe with relative abundance data
heatmap_plot <- plot_protein_heatmap(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "PINK1.KO")
)

# Print the plot
print(heatmap_plot)

```
z-score

```{r}
#library(ggplot2)
#library(dplyr)
#library(tidyr)

#data = df
#proteins = top_genes
#sample_patterns =  c("Control", "PINK1.KO")

# Function to plot heatmap of relative abundance with Z-scores
plot_protein_heatmap_zscore <- function(data, proteins, sample_patterns, na_color = "grey"){
  # Filter the data for selected proteins
  data_filtered <- data %>%
    filter(Symbol %in% proteins)
  
  # Set the order of the Symbol factor based on the input vector 'proteins'
  data_filtered$Symbol <- factor(data_filtered$Symbol, levels = proteins)

  # Identify sample columns matching patterns
  sample_columns <- colnames(data_filtered)[sapply(colnames(data_filtered), function(col_name) {
    any(sapply(sample_patterns, function(p) grepl(p, col_name)))
  })]

  # Debug: Check contents of sample_columns
  print(paste("Sample columns selected:", paste(sample_columns, collapse = ", ")))

  # Check if sample_columns has valid entries
  if (length(sample_columns) == 0) {
    stop("No sample columns matched the patterns provided.")
  }

  # Select only columns matching sample patterns and the Symbol column
  data_filtered <- data_filtered %>%
    dplyr::select(Symbol, all_of(sample_columns))

  # Remove rows where all values are NA (excluding the Symbol column)
  data_filtered <- data_filtered %>%
    filter(rowSums(is.na(dplyr::select(., -Symbol))) < length(sample_columns))

  # Calculate Z-scores for each protein across the selected samples
  data_zscore <- data_filtered %>%
    mutate(across(all_of(sample_columns), ~ scale(.)[, 1], .names = "z_{col}"))
  # Reshape data for ggplot
  data_long <- data_zscore %>%
    pivot_longer(
      cols = starts_with("z_"), 
      names_to = "Sample", 
      values_to = "Abundance"
    ) %>%
    mutate(Sample = gsub("z_", "", Sample))  # Remove 'z_' prefix for clean sample names
  # Create the heatmap
  heatmap_plot <- ggplot(data_long, aes(x = Sample, y = Symbol, fill = Abundance)) +
    geom_tile(color = "white") +
    scale_fill_gradientn(
    #colors = c("#ffffff", "#ffcccc", "#ff6666", "#ff3333","#fa0505", "#cc0000","#990000"),
    #colors = c("#fdfef4", "#DAF7A6", "#FFC300", "#FF5733","#e71f05","#4d0b02"),
    colors = c("snow","lightgoldenrod1","gold1","darkorange1","red2","firebrick4"),
    values = scales::rescale(c(-0.5, -0.25, 0, 1,2,2.5,2.75)),
    na.value = na_color,
    guide = guide_colorbar(
      barwidth = 1,
      barheight = 10,
      title.position = "top",
      title.hjust = 0.5
    )) +
    theme_minimal() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
    labs(title = "Protein Abundance Heatmap (Z-Score)", x = "Samples", y = "Proteins")
  
  return(heatmap_plot)
}

# Example usage
heatmap_plot <- plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "PINK1.KO")
)

# Print the plot
print(heatmap_plot)




```


Adjust the function

```{r}

library(ggplot2)
library(dplyr)
library(tidyr)

# Function to plot heatmap of relative abundance with Z-scores
plot_protein_heatmap_zscore <- function(data, proteins, sample_patterns, colors, scale_values, na_color = "grey") {
  # Filter the data for selected proteins
  data_filtered <- data %>%
    filter(Symbol %in% proteins)
  
  # Set the order of the Symbol factor based on the input vector 'proteins'
  data_filtered$Symbol <- factor(data_filtered$Symbol, levels = proteins)

  # Identify sample columns matching patterns
  sample_columns <- colnames(data_filtered)[sapply(colnames(data_filtered), function(col_name) {
    any(sapply(sample_patterns, function(p) grepl(p, col_name)))
  })]

  # Debug: Check contents of sample_columns
  print(paste("Sample columns selected:", paste(sample_columns, collapse = ", ")))

  # Check if sample_columns has valid entries
  if (length(sample_columns) == 0) {
    stop("No sample columns matched the patterns provided.")
  }

  # Select only columns matching sample patterns and the Symbol column
  data_filtered <- data_filtered %>%
    dplyr::select(Symbol, all_of(sample_columns))

  # Remove rows where all values are NA (excluding the Symbol column)
  data_filtered <- data_filtered %>%
    filter(rowSums(is.na(dplyr::select(., -Symbol))) < length(sample_columns))

  # Calculate Z-scores for each protein across the selected samples
  data_zscore <- data_filtered %>%
    mutate(across(all_of(sample_columns), ~ scale(.)[, 1], .names = "z_{col}"))

  # Reshape data for ggplot
  data_long <- data_zscore %>%
    pivot_longer(
      cols = starts_with("z_"), 
      names_to = "Sample", 
      values_to = "Abundance"
    ) %>%
    mutate(Sample = gsub("z_", "", Sample))  # Remove 'z_' prefix for clean sample names

  # Create the heatmap
  heatmap_plot <- ggplot(data_long, aes(x = Sample, y = Symbol, fill = Abundance)) +
    geom_tile(color = "white") +
    scale_fill_gradientn(
      colors = colors,
      values = scales::rescale(scale_values),
      na.value = na_color,
      guide = guide_colorbar(
        barwidth = 1,
        barheight = 10,
        title.position = "top",
        title.hjust = 0.5
      )
    ) +
    theme_minimal() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
    labs(title = "Protein Abundance Heatmap (Z-Score)", x = "Samples", y = "Proteins")
  
  return(heatmap_plot)
}

# Example usage
heatmap_plot <- plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "PINK1.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-2.5, -2, -1, 0, 1, 2, 2.5) # Adjust based on your data range
)

# Print the plot
print(heatmap_plot)


```


```{r}
library(ggplot2)
library(dplyr)
library(tidyr)

# Function to plot heatmap of relative abundance with Z-scores
plot_protein_heatmap_zscore <- function(data, proteins, sample_patterns, colors, scale_values, na_color = "grey", group_means = FALSE) {
  # Filter the data for selected proteins
  data_filtered <- data %>%
    filter(Symbol %in% proteins)
  
  # Set the order of the Symbol factor based on the input vector 'proteins'
  data_filtered$Symbol <- factor(data_filtered$Symbol, levels = proteins)

  # Identify sample columns matching patterns
  sample_columns <- colnames(data_filtered)[sapply(colnames(data_filtered), function(col_name) {
    any(sapply(sample_patterns, function(p) grepl(p, col_name)))
  })]

  # Debug: Check contents of sample_columns
  print(paste("Sample columns selected:", paste(sample_columns, collapse = ", ")))

  # Check if sample_columns has valid entries
  if (length(sample_columns) == 0) {
    stop("No sample columns matched the patterns provided.")
  }

  # Select only columns matching sample patterns and the Symbol column
  data_filtered <- data_filtered %>%
    dplyr::select(Symbol, all_of(sample_columns))

  # Remove rows where all values are NA (excluding the Symbol column)
  data_filtered <- data_filtered %>%
    filter(rowSums(is.na(dplyr::select(., -Symbol))) < length(sample_columns))

  if (group_means) {
    # Group samples by the base name and calculate mean
    sample_base <- gsub("\\.\\d+$", "", colnames(data_filtered)[-1])
    data_grouped <- data_filtered %>%
      pivot_longer(cols = -Symbol, names_to = "Sample", values_to = "Abundance") %>%
      mutate(SampleBase = gsub("\\.\\d+$", "", Sample)) %>%
      group_by(Symbol, SampleBase) %>%
      summarize(Abundance = mean(Abundance, na.rm = TRUE), .groups = 'drop') %>%
      pivot_wider(names_from = SampleBase, values_from = Abundance)

    # Calculate Z-scores
    data_zscore <- data_grouped %>%
      mutate(across(-Symbol, ~ scale(.)[, 1], .names = "z_{col}"))

    # Reshape data for ggplot
    data_long <- data_zscore %>%
      pivot_longer(
        cols = starts_with("z_"), 
        names_to = "Sample", 
        values_to = "Abundance"
      ) %>%
      mutate(Sample = gsub("z_", "", Sample))  # Remove 'z_' prefix for clean sample names
  } else {
    # Calculate Z-scores for each protein across the selected samples
    data_zscore <- data_filtered %>%
      mutate(across(all_of(sample_columns), ~ scale(.)[, 1], .names = "z_{col}"))

    # Reshape data for ggplot
    data_long <- data_zscore %>%
      pivot_longer(
        cols = starts_with("z_"), 
        names_to = "Sample", 
        values_to = "Abundance"
      ) %>%
      mutate(Sample = gsub("z_", "", Sample))  # Remove 'z_' prefix for clean sample names
  }

  # Create the heatmap
  heatmap_plot <- ggplot(data_long, aes(x = Sample, y = Symbol, fill = Abundance)) +
    geom_tile(color = "white") +
    scale_fill_gradientn(
      colors = colors,
      values = scales::rescale(scale_values),
      na.value = na_color,
      guide = guide_colorbar(
        barwidth = 1,
        barheight = 10,
        title.position = "top",
        title.hjust = 0.5
      )
    ) +
    theme_minimal() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
    labs(title = "Protein Abundance Heatmap (Z-Score)", x = "Samples", y = "Proteins")
  
  return(heatmap_plot)
}

# Example usage
heatmap_plot <- plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "PINK1.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-2.5, -2, -1, 0, 1, 2, 2.5), # Adjust based on your data range
  group_means = TRUE # Set to FALSE if you want individual samples
)

# Print the plot
print(heatmap_plot)


# Example usage
heatmap_plot <- plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "PINK1.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-2.5, -2, -1, 0, 1, 2, 2.5), # Adjust based on your data range
  group_means = FALSE # Set to FALSE if you want individual samples
)

# Print the plot
print(heatmap_plot)


```


Control width

```{r}
library(ggplot2)
library(dplyr)
library(tidyr)

# Function to plot heatmap of relative abundance with Z-scores
plot_protein_heatmap_zscore <- function(data, proteins, sample_patterns, colors, scale_values, na_color = "grey", group_means = FALSE, tile_width = 0.9) {
  # Filter the data for selected proteins
  data_filtered <- data %>%
    filter(Symbol %in% proteins)
  
  # Set the order of the Symbol factor based on the input vector 'proteins'
  data_filtered$Symbol <- factor(data_filtered$Symbol, levels = proteins)

  # Identify sample columns matching patterns
  sample_columns <- colnames(data_filtered)[sapply(colnames(data_filtered), function(col_name) {
    any(sapply(sample_patterns, function(p) grepl(p, col_name)))
  })]

  # Debug: Check contents of sample_columns
  print(paste("Sample columns selected:", paste(sample_columns, collapse = ", ")))

  # Check if sample_columns has valid entries
  if (length(sample_columns) == 0) {
    stop("No sample columns matched the patterns provided.")
  }

  # Select only columns matching sample patterns and the Symbol column
  data_filtered <- data_filtered %>%
    dplyr::select(Symbol, all_of(sample_columns))

  # Remove rows where all values are NA (excluding the Symbol column)
  data_filtered <- data_filtered %>%
    filter(rowSums(is.na(dplyr::select(., -Symbol))) < length(sample_columns))

  if (group_means) {
    # Group samples by the base name and calculate mean
    sample_base <- gsub("\\.\\d+$", "", colnames(data_filtered)[-1])
    data_grouped <- data_filtered %>%
      pivot_longer(cols = -Symbol, names_to = "Sample", values_to = "Abundance") %>%
      mutate(SampleBase = gsub("\\.\\d+$", "", Sample)) %>%
      group_by(Symbol, SampleBase) %>%
      summarize(Abundance = mean(Abundance, na.rm = TRUE), .groups = 'drop') %>%
      pivot_wider(names_from = SampleBase, values_from = Abundance)

    # Calculate Z-scores
    data_zscore <- data_grouped %>%
      mutate(across(-Symbol, ~ scale(.)[, 1], .names = "z_{col}"))

    # Reshape data for ggplot
    data_long <- data_zscore %>%
      pivot_longer(
        cols = starts_with("z_"), 
        names_to = "Sample", 
        values_to = "Abundance"
      ) %>%
      mutate(Sample = gsub("z_", "", Sample))  # Remove 'z_' prefix for clean sample names
  } else {
    # Calculate Z-scores for each protein across the selected samples
    data_zscore <- data_filtered %>%
      mutate(across(all_of(sample_columns), ~ scale(.)[, 1], .names = "z_{col}"))

    # Reshape data for ggplot
    data_long <- data_zscore %>%
      pivot_longer(
        cols = starts_with("z_"), 
        names_to = "Sample", 
        values_to = "Abundance"
      ) %>%
      mutate(Sample = gsub("z_", "", Sample))  # Remove 'z_' prefix for clean sample names
  }

  # Create the heatmap
  heatmap_plot <- ggplot(data_long, aes(x = Sample, y = Symbol, fill = Abundance)) +
    geom_tile(color = "white") +
    scale_fill_gradientn(
      colors = colors,
      values = scales::rescale(scale_values),
      na.value = na_color,
      guide = guide_colorbar(
        barwidth = 1,
        barheight = 10,
        title.position = "top",
        title.hjust = 0.5
      )
    ) +
    theme_minimal() +
    theme(
      axis.text.x = element_text(angle = 45, hjust = 1),
      aspect.ratio = 1 / tile_width # Adjust aspect ratio
    ) +
    labs(title = "Protein Abundance Heatmap (Z-Score)", x = "Samples", y = "Proteins")
  
  return(heatmap_plot)
}

# Example usage
heatmap_plot <- plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "PINK1.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-2.5, -2, -1, 0, 1, 2, 2.5), # Adjust based on your data range
  group_means = TRUE, # Set to FALSE if you want individual samples
  tile_width = 0.25 # Adjust the width of the tiles (default is 0.9)
)

# Print the plot
print(heatmap_plot)


```

Function to see the gene expression
```{r}

# Function to plot heatmap of relative abundance with Z-scores
protein_zscore <- function(data, proteins, sample_patterns, group_means = FALSE) {
  # Filter the data for selected proteins
  data_filtered <- data %>%
    filter(Symbol %in% proteins)
  
  # Set the order of the Symbol factor based on the input vector 'proteins'
  data_filtered$Symbol <- factor(data_filtered$Symbol, levels = proteins)

  # Identify sample columns matching patterns
  sample_columns <- colnames(data_filtered)[sapply(colnames(data_filtered), function(col_name) {
    any(sapply(sample_patterns, function(p) grepl(p, col_name)))
  })]

  # Debug: Check contents of sample_columns
  print(paste("Sample columns selected:", paste(sample_columns, collapse = ", ")))

  # Check if sample_columns has valid entries
  if (length(sample_columns) == 0) {
    stop("No sample columns matched the patterns provided.")
  }

  # Select only columns matching sample patterns and the Symbol column
  data_filtered <- data_filtered %>%
    dplyr::select(Symbol, all_of(sample_columns))

  # Remove rows where all values are NA (excluding the Symbol column)
  data_filtered <- data_filtered %>%
    filter(rowSums(is.na(dplyr::select(., -Symbol))) < length(sample_columns))

  if (group_means) {
    # Group samples by the base name and calculate mean
    sample_base <- gsub("\\.\\d+$", "", colnames(data_filtered)[-1])
    data_grouped <- data_filtered %>%
      pivot_longer(cols = -Symbol, names_to = "Sample", values_to = "Abundance") %>%
      mutate(SampleBase = gsub("\\.\\d+$", "", Sample)) %>%
      group_by(Symbol, SampleBase) %>%
      summarize(Abundance = mean(Abundance, na.rm = TRUE), .groups = 'drop') %>%
      pivot_wider(names_from = SampleBase, values_from = Abundance)

    # Calculate Z-scores
    data_zscore <- data_grouped %>%
      mutate(across(-Symbol, ~ scale(.)[, 1], .names = "z_{col}"))

    # Reshape data for ggplot
    data_long <- data_zscore %>%
      pivot_longer(
        cols = starts_with("z_"), 
        names_to = "Sample", 
        values_to = "Abundance"
      ) %>%
      mutate(Sample = gsub("z_", "", Sample))  # Remove 'z_' prefix for clean sample names
  } else {
    # Calculate Z-scores for each protein across the selected samples
    data_zscore <- data_filtered %>%
      mutate(across(all_of(sample_columns), ~ scale(.)[, 1], .names = "z_{col}"))

    # Reshape data for ggplot
    data_long <- data_zscore %>%
      pivot_longer(
        cols = starts_with("z_"), 
        names_to = "Sample", 
        values_to = "Abundance"
      ) %>%
      mutate(Sample = gsub("z_", "", Sample))  # Remove 'z_' prefix for clean sample names
  }
  
  return(data_long)
}

df.long <- protein_zscore(data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "PINK1.KO"), group_means = TRUE) 
  
max(df.long$Abundance)
min(df.long$Abundance)
  
```





Check each contrast

```{r}

top_genes <- select_top_genes(filtered_DEP$`PINK1-KO`, n = 10)

plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "PINK1.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-0.5, -0.25, 0, 1, 2, 4), # Adjust based on your data range
  group_means = TRUE, # Set to FALSE if you want individual samples
   tile_width = 0.25
)

plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "PINK1.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-0.5, -0.25, 0, 1, 2, 4), # Adjust based on your data range
  group_means = FALSE,# Set to FALSE if you want individual samples
   tile_width = 0.25
)



```

```{r}

# PINK1 DEP
top_genes <- c("LMNA","DCN","HDGF","CA2","NEFL","NEFM","SYT2","STX1A")
df.long <- protein_zscore(data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "PINK1.KO"), group_means = TRUE) 
  
max(df.long$Abundance)
min(df.long$Abundance)


plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "PINK1.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-0.9,-0.6,-0.2, 0, 2, 2.5), # Adjust based on your data range
  group_means = TRUE, # Set to FALSE if you want individual samples
   tile_width = 0.25
)

plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "PINK1.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-0.9,-0.6,-0.2, 0, 2, 2.5), # Adjust based on your data range
  group_means = FALSE,# Set to FALSE if you want individual samples
   tile_width = 0.25
)

```

```{r}
colnames(df)
```



SNCA-A53T

```{r}
top_genes <- select_top_genes(filtered_DEP$`SNCA-A53T`, n = 10)

df.long <- protein_zscore(data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "A53T"), group_means = TRUE) 
  
max(df.long$Abundance)
min(df.long$Abundance)

plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "A53T"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-1, -0.5,-0.25, 0, 1, 2, 4), # Adjust based on your data range
  group_means = TRUE, # Set to FALSE if you want individual samples
   tile_width = 0.25
)

plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "A53T"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-1, -0.5, -0.25,0, 1, 2, 4), # Adjust based on your data range
  group_means = FALSE,# Set to FALSE if you want individual samples
   tile_width = 0.25
)

```
GBA
```{r}
top_genes <- select_top_genes(filtered_DEP$`GBA-KO`, n = 10)

df.long <- protein_zscore(data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "GBA.KO"), group_means = TRUE) 
  
max(df.long$Abundance)
min(df.long$Abundance)

plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "GBA.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-1, -0.5, -0.25,0, 1, 2, 3.5), # Adjust based on your data range
  group_means = TRUE, # Set to FALSE if you want individual samples
   tile_width = 0.25
)

plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "GBA.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-1, -0.5, -0.25,0, 1, 2, 3.5), # Adjust based on your data range
  group_means = FALSE,# Set to FALSE if you want individual samples
   tile_width = 0.25
)
```
Parkin  KO

```{r}
top_genes <- select_top_genes(filtered_DEP$`PRKN-KO`, n = 10)

df.long <- protein_zscore(data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "PRKN.KO"), group_means = TRUE) 
  
max(df.long$Abundance)
min(df.long$Abundance)

plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "PRKN.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-1, -0.5, -0.25,0, 1, 2, 3.5), # Adjust based on your data range
  group_means = TRUE, # Set to FALSE if you want individual samples
   tile_width = 0.25
)

plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "PRKN.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-1, -0.5, -0.25,0, 1, 2, 3.5), # Adjust based on your data range
  group_means = FALSE,# Set to FALSE if you want individual samples
   tile_width = 0.25
)

```

For the dark genome gene expression levels I'll need the other dataframe

```{r}

abundance.dark <- read.csv("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/ProtomicsNormGeneAbundance_dark.csv")

colnames(abundance.dark)

df <- abundance.dark[, c(3,5:16,18:20)]
df.dark <- abundance.dark[, c(3,5:16,18:20)]
colnames(df)




```


```{r}
top_genes <- select_top_genes(filtered_DEP$`IGSF9B-KO`, n = 10)

df.long <- protein_zscore(data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "IGSF9B.KO"), group_means = TRUE) 
  
max(df.long$Abundance)
min(df.long$Abundance)

plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "IGSF9B.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-1, -0.5, -0.25,0, 1, 2, 3.5), # Adjust based on your data range
  group_means = TRUE, # Set to FALSE if you want individual samples
   tile_width = 0.25
)

plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "IGSF9B.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-1, -0.5, -0.25,0, 1, 2, 3.5), # Adjust based on your data range
  group_means = FALSE,# Set to FALSE if you want individual samples
   tile_width = 0.25
)

```

```{r}
top_genes <- select_top_genes(filtered_DEP$`INPP5F-KO`, n = 10)

df.long <- protein_zscore(data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "INPP5F.KO"), group_means = TRUE) 
  
max(df.long$Abundance)
min(df.long$Abundance)

plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "INPP5F.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
  scale_values = c(-0.75, -0.5, -0.25,0, 1, 2, 4), # Adjust based on your data range
  group_means = TRUE, # Set to FALSE if you want individual samples
   tile_width = 0.25
)

plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "INPP5F.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
  scale_values = c(-0.75, -0.5, -0.25,0, 1, 2, 4), # Adjust based on your data range
  group_means = FALSE,# Set to FALSE if you want individual samples
   tile_width = 0.25
)

```


```{r}

top_genes <- select_top_genes(filtered_DEP$`IP6K2-KO`, n = 10)

df.long <- protein_zscore(data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "IP6K2.KO"), group_means = TRUE) 
  
max(df.long$Abundance)
min(df.long$Abundance)

plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "IP6K2.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
  scale_values = c(-0.75, -0.5, -0.25,0, 1, 2, 4), # Adjust based on your data range
  group_means = TRUE, # Set to FALSE if you want individual samples
   tile_width = 0.25
)

plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "IP6K2.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
  scale_values = c(-0.75, -0.5, -0.25,0, 1, 2, 4), # Adjust based on your data range
  group_means = FALSE,# Set to FALSE if you want individual samples
   tile_width = 0.25
)



```


```{r}

top_genes <- select_top_genes(filtered_DEP$`SH3GL2-KO`, n = 10)

df.long <- protein_zscore(data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "SH3GL2.KO"), group_means = TRUE) 
  
max(df.long$Abundance)
min(df.long$Abundance)

plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "SH3GL2.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
  scale_values = c(-0.6, -0.5,0.25, 0, 1, 3.8, 4.1), # Adjust based on your data range
  group_means = TRUE, # Set to FALSE if you want individual samples
   tile_width = 0.25
)

plot_protein_heatmap_zscore(
  data = df,
  proteins = top_genes, # Example protein names
  sample_patterns = c("Control", "SH3GL2.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
  scale_values = c(-0.6, -0.5,0.25, 0, 1, 3.8, 4.1), # Adjust based on your data range
  group_means = FALSE,# Set to FALSE if you want individual samples
   tile_width = 0.25
)


```

```{r}
names(filtered_DEP)


```


# gene overlap

```{r}

library(UpSetR)


# Extract gene lists for each comparison in filtered_DEP
gene_lists <- lapply(filtered_DEP, function(df) unique(df$Symbol))

# Name the lists according to the contrast names
names(gene_lists) <- names(filtered_DEP)

# Create a combined list of all unique genes
all_genes <- unique(unlist(gene_lists))

# Initialize a data frame to store binary presence/absence data
gene_matrix <- data.frame(Symbol = all_genes)

# Loop through each comparison and create binary columns
for (contrast in names(gene_lists)) {
  gene_matrix[[contrast]] <- as.numeric(gene_matrix$Symbol %in% gene_lists[[contrast]])
}

# Debug: Check the structure of the gene_matrix
str(gene_matrix)


# Create the UpSet plot
upset(
  gene_matrix,
  sets = names(gene_lists),
  sets.bar.color = "#56B4E9",
  order.by = "freq",
  empty.intersections = "on",
  keep.order = TRUE
)




```

Control the order 
```{r}
# Install UpSetR package if not already installed
if (!requireNamespace("UpSetR", quietly = TRUE)) {
  install.packages("UpSetR")
}

# Load the required libraries
library(UpSetR)
library(dplyr)

create_upset_plot <- function(filtered_DEP, contrast_order, colors, text_scale = 1.5) {
  # Ensure the length of colors matches the length of contrast_order
  if (length(colors) != length(contrast_order)) {
    stop("The length of the 'colors' vector must match the length of 'contrast_order'.")
  }
  
  # Extract gene lists for each comparison in filtered_DEP
  gene_lists <- lapply(filtered_DEP, function(df) unique(df$Symbol))
  
  # Name the lists according to the contrast names
  names(gene_lists) <- names(filtered_DEP)
  
  # Create a combined list of all unique genes
  all_genes <- unique(unlist(gene_lists))
  
  # Initialize a data frame to store binary presence/absence data
  gene_matrix <- data.frame(Symbol = all_genes)
  
  # Loop through each comparison and create binary columns
  for (contrast in names(gene_lists)) {
    gene_matrix[[contrast]] <- as.numeric(gene_matrix$Symbol %in% gene_lists[[contrast]])
  }
  
  # Reorder the gene_matrix columns to match the desired contrast order
  gene_matrix <- gene_matrix %>%
    dplyr::select(Symbol, all_of(contrast_order))

  # Debug: Check the structure of the gene_matrix
  print(str(gene_matrix))
  
  # Create the UpSet plot with custom colors
  upset(
    gene_matrix,
    sets = contrast_order,  # Use the specified order for contrasts
    sets.bar.color = colors, # Apply the specified colors in order
    order.by = "freq",
    empty.intersections = "on",
    keep.order = TRUE,
    text.scale = text_scale
  )
}


colors <-c("#d34a98","#439472","#5c1c97","#9e69cf","#931ea0","#148825","#db072f","#111e8f")

# Example usage with specified contrast order
contrast_order <- rev(c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", 
                    "IGSF9B-KO","INPP5F-KO", "SH3GL2-KO","IP6K2-KO"))

p <- create_upset_plot(filtered_DEP, contrast_order, colors = rev(colors), text_scale = 1.5)
print(p)

```

```{r}
png("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/UpsetplotBrightandDark.png", width = 1000, height = 500)
p
dev.off()

pdf("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/UpsetplotBrightandDark.pdf", width = 10, height = 5.5)
p
dev.off()

```




See which genes overlap - function 

```{r}


# Function to extract overlapping and unique genes
get_gene_overlap_and_unique <- function(filtered_DEP, contrast_list) {
  
  # Check if all contrasts in contrast_list exist in filtered_DEP
  missing_contrasts <- setdiff(contrast_list, names(filtered_DEP))
  if (length(missing_contrasts) > 0) {
    stop(paste("The following contrasts are not found in filtered_DEP:", paste(missing_contrasts, collapse = ", ")))
  }

  # Extract gene lists for each specified contrast
  gene_lists <- lapply(contrast_list, function(contrast) unique(filtered_DEP[[contrast]]$Symbol))
  names(gene_lists) <- contrast_list

  # Find overlapping genes (common to all contrasts in the input list)
  overlapping_genes <- Reduce(intersect, gene_lists)
  
  # Find unique genes for each contrast
  unique_genes <- lapply(names(gene_lists), function(contrast) {
    # Genes in the current contrast but not in any other contrast from the list
    setdiff(gene_lists[[contrast]], unlist(gene_lists[names(gene_lists) != contrast]))
  })
  names(unique_genes) <- names(gene_lists)

  # Create a list to store the results
  result_list <- list(
    "Overlapping Genes" = overlapping_genes,
    "Unique Genes" = unique_genes
  )
  
  return(result_list)
}






# bright genome overlap
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO","PRKN-KO") # Specify the contrasts of interest

result.bright <- get_gene_overlap_and_unique(filtered_DEP, contrast_list)

# Print results
print(result.bright$`Overlapping Genes`) # Genes common across all specified contrasts
#print(result$`Unique Genes`) # List of genes unique to each contrast



```

Function isn't exactly correct need to fix


```{r}

pink.df <- filtered_DEP$`PINK1-KO`
dim(pink.df)
prkn.df <- filtered_DEP$`PRKN-KO`
dim(prkn.df)
intersect(pink.df$Accession, prkn.df$Accession)


```
```{r}
# apply to filtered list
regulation_summary <- summarize_regulations(filtered_DEP)
print(regulation_summary)
```

```{r}
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO","PRKN-KO","IGSF9B-KO","INPP5F-KO","IP6K2-KO","IGSF9B-KO", "INPP5F-KO", "IP6K2-KO") # Specify the contrasts of interest

all <- get_gene_overlap_and_unique(filtered_DEP, contrast_list)
print(all$`Overlapping Genes`)



```


```{r}
names(filtered_DEP)
```

```{r}
contrast_list <- c("GBA-KO", "PINK1-KO") # Specify the contrasts of interest

gba.pink1 <- get_gene_overlap_and_unique(filtered_DEP, contrast_list)

print(gba.pink1)
 # Genes common across all specified contrasts
#print(result$`Unique Genes`) # List of genes unique to each contrast

```




```{r}
# bright genome overlap
contrast_list <- c("IGSF9B-KO", "INPP5F-KO","IP6K2-KO","SH3GL2-KO") # Specify the contrasts of interest

result.dark <- get_gene_overlap_and_unique(filtered_DEP, contrast_list)

# Print results
print(result.dark$`Overlapping Genes`) # Genes common across all specified contrasts
#print(result.dark$`Unique Genes`) # List of genes unique to each contrast


```

Look at overlap in contrasts that also have targeted pathways changes that match


```{r}

# PRKN KO, IGSF9B and SH3GL2 all are down regulated in TH + levels
contrast_list <- c("PRKN-KO","IGSF9B-KO","SH3GL2-KO") # Specify the contrasts of interest

result.THdown <- get_gene_overlap_and_unique(filtered_DEP, contrast_list)

# Print results
print(result.THdown$`Overlapping Genes`) # Genes common across all specified contrasts


```

```{r}

df.long <- protein_zscore(data = df,
  proteins = result.THdown$`Overlapping Genes`, # Example protein names
  sample_patterns = c("Control","PRKN.KO","IGSF9B" ,"SH3GL2.KO"), group_means = TRUE) 
  
max(df.long$Abundance)
min(df.long$Abundance)

plot_protein_heatmap_zscore(
  data = df,
  proteins = result.THdown$`Overlapping Genes`, # Example protein names
  sample_patterns = c("Control","PRKN.KO","IGSF9B.KO" ,"SH3GL2.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
  scale_values = c(-1, -0.5,-0.25, 0, 1, 2.5, 4.5), # Adjust based on your data range
  group_means = TRUE,# Set to FALSE if you want individual samples
   tile_width = 0.25
) 




```

Function to plot grouped by expression


```{r}
# Load required libraries
library(dplyr)
library(tidyr)
library(pheatmap)

# Function to plot heatmap of relative abundance with Z-scores and clustering
plot_clustered_protein_heatmap_zscore <- function(data, proteins, sample_patterns, colors, scale_values, na_color = "grey", group_means = FALSE) {
  # Filter the data for selected proteins
  data_filtered <- data %>%
    filter(Symbol %in% proteins)
  
  # Set the order of the Symbol factor based on the input vector 'proteins'
  data_filtered$Symbol <- factor(data_filtered$Symbol, levels = proteins)

  # Identify sample columns matching patterns
  sample_columns <- colnames(data_filtered)[sapply(colnames(data_filtered), function(col_name) {
    any(sapply(sample_patterns, function(p) grepl(p, col_name)))
  })]

  # Debug: Check contents of sample_columns
  print(paste("Sample columns selected:", paste(sample_columns, collapse = ", ")))

  # Check if sample_columns has valid entries
  if (length(sample_columns) == 0) {
    stop("No sample columns matched the patterns provided.")
  }

  # Select only columns matching sample patterns and the Symbol column
  data_filtered <- data_filtered %>%
    dplyr::select(Symbol, all_of(sample_columns))

  # Remove rows where all values are NA (excluding the Symbol column)
  data_filtered <- data_filtered %>%
    filter(rowSums(is.na(dplyr::select(., -Symbol))) < length(sample_columns))

  if (group_means) {
    # Group samples by the base name and calculate mean
    sample_base <- gsub("\\.\\d+$", "", colnames(data_filtered)[-1])
    data_grouped <- data_filtered %>%
      pivot_longer(cols = -Symbol, names_to = "Sample", values_to = "Abundance") %>%
      mutate(SampleBase = gsub("\\.\\d+$", "", Sample)) %>%
      group_by(Symbol, SampleBase) %>%
      summarize(Abundance = mean(Abundance, na.rm = TRUE), .groups = 'drop') %>%
      pivot_wider(names_from = SampleBase, values_from = Abundance)

    # Calculate Z-scores
    data_zscore <- data_grouped %>%
      mutate(across(-Symbol, ~ scale(.)[, 1], .names = "{col}"))

    # Convert data to matrix form for pheatmap
    data_matrix <- as.matrix(data_zscore[,-1])  # Exclude the Symbol column from matrix conversion
    rownames(data_matrix) <- data_zscore$Symbol # Set row names to the Symbol column
  } else {
    # Calculate Z-scores for each protein across the selected samples
    data_zscore <- data_filtered %>%
      mutate(across(all_of(sample_columns), ~ scale(.)[, 1], .names = "{col}"))

    # Convert data to matrix form for pheatmap
    data_matrix <- as.matrix(data_zscore[,-1])  # Exclude the Symbol column from matrix conversion
    rownames(data_matrix) <- data_zscore$Symbol # Set row names to the Symbol column
  }

  # Create the heatmap with hierarchical clustering
  pheatmap(
    data_matrix,
    cluster_rows = TRUE,
    cluster_cols = TRUE,
    color = colorRampPalette(colors)(100),
    na_col = na_color,
    main = "Clustered Protein Abundance Heatmap (Z-Score)"
  )
}

# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
  data = df,
  proteins = result.THdown$`Overlapping Genes`, # Example protein names
  sample_patterns = c("Control", "IGSF9B.KO", "SH3GL2.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values =c(-1, -0.5,-0.25, 0, 1, 2.5, 4.5), # Adjust based on your data range
  group_means = FALSE, # Set to FALSE if you want individual samples
  ) 



```
```{r}
# Load required libraries
library(dplyr)
library(tidyr)
library(pheatmap)

# Function to plot heatmap of relative abundance with Z-scores and clustering
plot_clustered_protein_heatmap_zscore <- function(data, proteins, sample_patterns, colors, scale_values, na_color = "grey", group_means = FALSE, cell_width = 10, cell_height = 10) {
  # Filter the data for selected proteins
  data_filtered <- data %>%
    filter(Symbol %in% proteins)
  
  # Set the order of the Symbol factor based on the input vector 'proteins'
  data_filtered$Symbol <- factor(data_filtered$Symbol, levels = proteins)

  # Identify sample columns matching patterns
  sample_columns <- colnames(data_filtered)[sapply(colnames(data_filtered), function(col_name) {
    any(sapply(sample_patterns, function(p) grepl(p, col_name)))
  })]

  # Debug: Check contents of sample_columns
  print(paste("Sample columns selected:", paste(sample_columns, collapse = ", ")))

  # Check if sample_columns has valid entries
  if (length(sample_columns) == 0) {
    stop("No sample columns matched the patterns provided.")
  }

  # Select only columns matching sample patterns and the Symbol column
  data_filtered <- data_filtered %>%
    dplyr::select(Symbol, all_of(sample_columns))

  # Remove rows where all values are NA (excluding the Symbol column)
  data_filtered <- data_filtered %>%
    filter(rowSums(is.na(dplyr::select(., -Symbol))) < length(sample_columns))

  if (group_means) {
    # Group samples by the base name and calculate mean
    sample_base <- gsub("\\.\\d+$", "", colnames(data_filtered)[-1])
    data_grouped <- data_filtered %>%
      pivot_longer(cols = -Symbol, names_to = "Sample", values_to = "Abundance") %>%
      mutate(SampleBase = gsub("\\.\\d+$", "", Sample)) %>%
      group_by(Symbol, SampleBase) %>%
      summarize(Abundance = mean(Abundance, na.rm = TRUE), .groups = 'drop') %>%
      pivot_wider(names_from = SampleBase, values_from = Abundance)

    # Calculate Z-scores
    data_zscore <- data_grouped %>%
      mutate(across(-Symbol, ~ scale(.)[, 1], .names = "{col}"))

    # Convert data to matrix form for pheatmap
    data_matrix <- as.matrix(data_zscore[,-1])  # Exclude the Symbol column from matrix conversion
    rownames(data_matrix) <- data_zscore$Symbol # Set row names to the Symbol column
  } else {
    # Calculate Z-scores for each protein across the selected samples
    data_zscore <- data_filtered %>%
      mutate(across(all_of(sample_columns), ~ scale(.)[, 1], .names = "{col}"))

    # Convert data to matrix form for pheatmap
    data_matrix <- as.matrix(data_zscore[,-1])  # Exclude the Symbol column from matrix conversion
    rownames(data_matrix) <- data_zscore$Symbol # Set row names to the Symbol column
  }

  # Create the heatmap with hierarchical clustering
  pheatmap(
    data_matrix,
    cluster_rows = TRUE,
    cluster_cols = TRUE,
    color = colorRampPalette(colors)(100),
    na_col = na_color,
    main = "Clustered Protein Abundance Heatmap (Z-Score)",
    cellwidth = cell_width,   # Adjust cell width
    cellheight = cell_height  # Adjust cell height
  )
}

# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
  data = df,
  proteins = result.THdown$`Overlapping Genes`, # Example protein names
  sample_patterns = c("Control", "IGSF9B.KO", "SH3GL2.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-1, -0.5,-0.25, 0, 1, 2.5, 4.5), # Adjust based on your data range
  group_means = TRUE, # Set to FALSE if you want individual samples
  cell_width = 20, # Control column width
  cell_height = 10 # Control row height
)

# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
  data = df,
  proteins = result.THdown$`Overlapping Genes`,  # Example protein names
  sample_patterns = c("Control", "IGSF9B.KO", "SH3GL2.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-1, -0.5,-0.25, 0, 1, 2.5, 4.5), # Adjust based on your data range
  group_means = FALSE, # Set to FALSE if you want individual samples
  cell_width = 20, # Control column width
  cell_height = 10 # Control row height
)



```


```{r}
contrast_list <- c("PRKN-KO","IGSF9B-KO","SH3GL2-KO", "IP6K2-KO") # Specify the contrasts of interest

result.GCasedown <- get_gene_overlap_and_unique(filtered_DEP, contrast_list)

# Print results
print(result.GCasedown$`Overlapping Genes`) # Genes common across all specified contrasts

```



Overlapping list of the genotypes with GCAse activity down: GBA-KO, PRNK-KO, INPP5F-KO, SH3GL2-KO, IP6K2-KO

```{r}

#colnames(df)

df.long <- protein_zscore(data = df,
  proteins = result.GCasedown$`Overlapping Genes`, # Example protein names
  sample_patterns = c("Control","PRKN.KO","IGSF9B" ,"SH3GL2.KO","IP6K2.KO"), group_means = TRUE) 
  
max(df.long$Abundance)
min(df.long$Abundance)


# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
  data = df,
  proteins = result.GCasedown$`Overlapping Genes`, # Example protein names
  sample_patterns = c("Control", "IGSF9B.KO", "SH3GL2.KO","IP6K2.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-1.5, -1,-0.5, 0, 1,2, 2.5), # Adjust based on your data range
  group_means = TRUE, # Set to FALSE if you want individual samples
  cell_width = 20, # Control column width
  cell_height = 10 # Control row height
)

# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
  data = df,
  proteins = result.GCasedown$`Overlapping Genes`,  # Example protein names
  sample_patterns = c("Control", "IGSF9B.KO", "SH3GL2.KO","IP6K2.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-1.5, -1,-0.5, 0, 1, 2, 2.5), # Adjust based on your data range
  group_means = FALSE, # Set to FALSE if you want individual samples
  cell_width = 20, # Control column width
  cell_height = 10 # Control row height
)



```

```{r}
contrast_list <- c("INPP5F-KO","SH3GL2-KO") # Specify the contrasts of interest

result.lyso <- get_gene_overlap_and_unique(filtered_DEP, contrast_list)

# Print results
print(result.lyso$`Overlapping Genes`) # Genes common across all specified contrasts

df.long <- protein_zscore(data = df,
  proteins = result.lyso$`Overlapping Genes`, # Example protein names
  sample_patterns = c("Control","INPP5F" ,"SH3GL2"), group_means = TRUE) 
  
max(df.long$Abundance)
min(df.long$Abundance)

pro.list <- result.lyso$`Overlapping Genes`

# list is too long



# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
  data = df,
  proteins = pro.list, # Example protein names
  sample_patterns = c("Control", "IGSF9B.KO", "SH3GL2.KO","IP6K2.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-0.5,-0.25, 0, 2.5, 4.5, 6.5), # Adjust based on your data range
  group_means = TRUE, # Set to FALSE if you want individual samples
  cell_width = 20, # Control column width
  cell_height = 10 # Control row height
)

# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
  data = df,
  proteins = pro.list,  # Example protein names
  sample_patterns = c("Control", "IGSF9B.KO", "SH3GL2.KO","IP6K2.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-0.5,-0.25, 0, 2.5, 4.5, 6.5), # Adjust based on your data range
  group_means = FALSE, # Set to FALSE if you want individual samples
  cell_width = 20, # Control column width
  cell_height = 10 # Control row height
)


# list is too long
```



```{r}
lysome <- read_excel("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/List of genes for RNAseq or Proteomics/LYSOSOME_GENE LIST.xlsx")
```

```{r}

```



Gene list 

```{r,fig.height=6}


df.long <- protein_zscore(data = df,
  proteins = lysome$`Gene name`, # Example protein names
  sample_patterns = c("Control","INPP5F.KO","IGSF9B.KO" ,"SH3GL2.KO","IP6K2.KO"), group_means = TRUE) 
  
max(df.long$Abundance)
min(df.long$Abundance)


# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
  data = df,
  proteins = lysome$`Gene name`, # Example protein names
  sample_patterns = c("Control", "INPP5F.KO","IGSF9B.KO", "SH3GL2.KO","IP6K2.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-1,-0.5, 0, 2.5,5), # Adjust based on your data range
  group_means = TRUE, # Set to FALSE if you want individual samples
  cell_width = 20, # Control column width
  cell_height = 10 # Control row height
)

# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
  data = df,
  proteins = lysome$`Gene name`,  # Example protein names
  sample_patterns = c("Control", "IGSF9B.KO", "SH3GL2.KO","IP6K2.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-1,-0.5, 0, 2.5,5), # Adjust based on your data range
  group_means = FALSE, # Set to FALSE if you want individual samples
  cell_width = 20, # Control column width
  cell_height = 10 # Control row height
)




plot_protein_heatmap_zscore(
  data = df,
  proteins = lysome$`Gene name`, # Example protein names
  sample_patterns = c("Control","IGSF9B.KO","INPP5F.KO","SH3GL2.KO","IP6K2.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
  scale_values = c(-1, -0.5,-0.25, 0, 1, 2.5, 5.5), # Adjust based on your data range
  group_means = TRUE,# Set to FALSE if you want individual samples
   tile_width = 0.25
) 




plot_protein_heatmap_zscore(
  data = df,
  proteins = lysome$`Gene name`, # Example protein names
  sample_patterns = c("Control","INPP5F.KO","SH3GL2.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
  scale_values = c(-1, -0.5,-0.25, 0, 1, 2.5, 5.5), # Adjust based on your data range
  group_means = TRUE,# Set to FALSE if you want individual samples
   tile_width = 0.25
) 





```

get overlap lists

```{r}
colnames(df)
```




```{r}
contrast_list <- c("INPP5F-KO","SH3GL2-KO") # Specify the contrasts of interest

result.lyso <- get_gene_overlap_and_unique(filtered_DEP, contrast_list)

print(result.lyso$`Overlapping Genes`)

#write.csv(result.lyso$`Overlapping Genes`)


contrast_list <- c("SNCA-A53T","PINK1-KO","PRKN-KO") # Specify the contrasts of interest
a53.pink.parkin <- get_gene_overlap_and_unique(filtered_DEP, contrast_list)




contrast_list <- c("INPP5F-KO","SH3GL2-KO","IP6K2-KO","IGSF9B-KO") # Specify the contrasts of interest
dark.ol <- get_gene_overlap_and_unique(filtered_DEP, contrast_list)

print(dark.ol)

print(a53.pink.parkin)

filtered_DEP$`GBA-KO`


df.gba <- filtered_DEP$`GBA-KO`

df.inpp <- 

```

specify group function to get the specific overlap list in the upset plot


```{r}

# this code should get the genes overlapping in the contrast list and NOT found in the other contrasts

# Function to extract genes overlapping in contrast_list but not in other contrasts
get_specific_gene_overlap <- function(filtered_DEP, contrast_list) {
  
  # Check if all contrasts in contrast_list exist in filtered_DEP
  missing_contrasts <- setdiff(contrast_list, names(filtered_DEP))
  if (length(missing_contrasts) > 0) {
    stop(paste("The following contrasts are not found in filtered_DEP:", paste(missing_contrasts, collapse = ", ")))
  }

  # Extract gene lists for contrasts in the contrast_list
  gene_lists_included <- lapply(contrast_list, function(contrast) unique(filtered_DEP[[contrast]]$Symbol))
  names(gene_lists_included) <- contrast_list

  # Extract gene lists for contrasts not in the contrast_list
  other_contrasts <- setdiff(names(filtered_DEP), contrast_list)
  gene_lists_excluded <- lapply(other_contrasts, function(contrast) unique(filtered_DEP[[contrast]]$Symbol))

  # Find overlapping genes among the specified contrasts in contrast_list
  overlapping_genes_included <- Reduce(intersect, gene_lists_included)
  
  # Find genes that are in the excluded contrasts
  genes_in_excluded <- unique(unlist(gene_lists_excluded))
  
  # Exclude genes that are in the excluded contrasts from the overlapping genes
  final_genes <- setdiff(overlapping_genes_included, genes_in_excluded)
  
  return(final_genes)
}

# Example usage
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)

# Print the result
print(overlap_genes)

length(overlap_genes) # 40 is the same lenght as the upset plot

# Example usage
contrast_list <- c("GBA-KO", "PINK1-KO", "PRKN-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)

# Print the result
print(overlap_genes)

length(overlap_genes)

write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/GBA_PINK1_PRKN_DPE.csv")


```

Make list corresponding to the upset plot to save and send to Roxanne 


```{r}

# pink parkin
contrast_list <- c("PINK1-KO", "PRKN-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of PINK1 PRKN")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/PINK1_PRKN_DPE.csv")
print("saved")

# PRKN IGSF9B
contrast_list <- c("IGSF9B-KO", "PRKN-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B PRKN")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_PRKN_DPE.csv")
print("saved")

# IGSF9B pink parkin
contrast_list <- c("PINK1-KO", "PRKN-KO", "IGSF9B-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B PINK1 PRKN")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_PINK1_PRKN_DPE.csv")
print("saved")

# PRKN IGSF9B
contrast_list <- c("IGSF9B-KO", "PINK1-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B PINK1")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_PINK1_DPE.csv")
print("saved")


# INPP5F IGSF9B
contrast_list <- c("IGSF9B-KO", "INPP5F-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B INPP5F")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_INPP5F_DPE.csv")
print("saved")


# INPP5F SH3GL2
contrast_list <- c("SH3GL2-KO", "INPP5F-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of SH3GL2 INPP5F")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SH3GL2_INPP5F_DPE.csv")
print("saved")


# SNCA A53T pink parkin
contrast_list <- c("PINK1-KO", "PRKN-KO", "SNCA-A53T")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of SNCA A53T PINK1 PRKN")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SNCAA53T_PINK1_PRKN_DPE.csv")
print("saved")

# bright genome and IGSF9B
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of SNCA GBA PINK1 PRKN IGSF9B")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SNCAA53T_GBA_PINK1_PRKN_IGSF9B_DPE.csv")
print("saved")



# bright genome
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of SNCA GBA PINK1 PRKN")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SNCAA53T_GBA_PINK1_PRKN_DPE.csv")
print("saved")


```

```{r}


contrast_list <- c("SH3GL2-KO", "INPP5F-KO","IGSF9B-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of SH3GL2 INPP5F IGSF9B")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_INPP5F_SH3GL2_DPE.csv")
print("saved")


# IGSF9B pink parkin SNCA
contrast_list <- c("PINK1-KO", "PRKN-KO", "IGSF9B-KO","SNCA-A53T")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B PINK1 PRKN SNCA-A53T")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SNCAA53T_IGSF9B_PINK1_PRKN_DPE.csv")
print("saved")


contrast_list <- c("PINK1-KO", "PRKN-KO", "IGSF9B-KO","GBA-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B PINK1 PRKN GBA")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/GBA_IGSF9B_PINK1_PRKN_DPE.csv")
print("saved")




```

```{r}


# A53T IGSF9B
contrast_list <- c("IGSF9B-KO", "SNCA-A53T")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B SNCA A53T")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_SNCAA53T_DPE.csv")
print("saved")

# A53T IGSF9B
contrast_list <- c("IGSF9B-KO", "SH3GL2-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B SH3GL2")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_SH3GL2_DPE.csv")
print("saved")

# A53T IGSF9B
contrast_list <- c("IGSF9B-KO", "IP6K2-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B IP6K")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_IP6K2_DPE.csv")
print("saved")


# A53T PINK1
contrast_list <- c("PINK1-KO", "SNCA-A53T")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of PINK1KO SNCA A53T")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/PINK1_SNCAA53T_DPE.csv")
print("saved")


# A53T PINK1 GBA
contrast_list <- c("PINK1-KO", "SNCA-A53T","GBA-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of PINK1KO SNCA A53T and GBA")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/PINK1_SNCAA53T_GBA_DPE.csv")
print("saved")


# A53T PINK1 IGSF9B
contrast_list <- c("PINK1-KO", "SNCA-A53T","IGSF9B-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of PINK1KO SNCA A53T and IGSF9B")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/PINK1_SNCAA53T_IGSF9B_DPE.csv")
print("saved")

# dark genome
contrast_list <- c("SH3GL2-KO", "INPP5F-KO","IGSF9B-KO", "IP6K2-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of SH3GL2 INPP5F IGSF9B")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_INPP5F_SH3GL2_IP6K2_DPE.csv")
print("saved")




```

Individual contrasts
```{r}

contrast_list <- c("SNCA-A53T")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to SNCA A53T")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SNCAA53T_DPE.csv")
print("saved")

contrast_list <- c("GBA-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to GBA KO")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/GBA_DPE.csv")
print("saved")

contrast_list <- c("PINK1-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to PINK1 KO")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/PINK1_DPE.csv")
print("saved")


contrast_list <- c("PRKN-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to PRKN KO")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/PRKN_DPE.csv")
print("saved")



contrast_list <- c("IGSF9B-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to IGSF9B")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_DPE.csv")
print("saved")

contrast_list <- c("INPP5F-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to INPP5F")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/INPP5F_DPE.csv")
print("saved")

contrast_list <- c("SH3GL2-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to SH3GL2")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SH3GL2_DPE.csv")
print("saved")

contrast_list <- c("IP6K2-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IP6K2")
print(overlap_genes)
length(overlap_genes)
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IP6K2_DPE.csv")
print("saved")

```


Make one dataframe for expression lists

```{r}
# look at each abundance dataframe

colnames(df.bright)
colnames(df.dark)

df <- merge(df.bright, df.dark, by.x = "Symbol", by.y = "Symbol")


```


Try some plots

```{r, fig.height=5}



df.long <- protein_zscore(data = df,
  proteins = lysome$`Gene name`, # Example protein names
  sample_patterns = c("Control","A53T","GBA.KO","PINK1.KO","PRKN.KO","INPP5F.KO","IGSF9B.KO" ,"SH3GL2.KO","IP6K2.KO"), group_means = TRUE) 
  
max(df.long$Abundance, na.rm = TRUE)
min(df.long$Abundance, na.rm = TRUE)


# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
  data = df,
  proteins = lysome$`Gene name`, # Example protein names
  sample_patterns = c("Control", "A53T","GBA.KO","PINK1.KO","PRKN.KO","INPP5F.KO","IGSF9B.KO", "SH3GL2.KO","IP6K2.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-1.5,-1,-0.5, 0, 2,4.5), # Adjust based on your data range
  group_means = TRUE, # Set to FALSE if you want individual samples
  cell_width = 20, # Control column width
  cell_height = 10 # Control row height
)



# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
  data = df,
  proteins = lysome$`Gene name`,  # Example protein names
  sample_patterns = c("Control.", "A53T.","GBA.KO","PINK1.KO","PRKN.KO","INPP5F.KO","IGSF9B.KO", "SH3GL2.KO","IP6K2.KO"),
  colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
  scale_values = c(-1,-0.5, 0, 2.5,5), # Adjust based on your data range
  group_means = FALSE, # Set to FALSE if you want individual samples
  cell_width = 20, # Control column width
  cell_height = 10 # Control row height
)




```

Heat map of Logfold change

```{r}

library(ggplot2)
library(dplyr)
library(tidyr)

# Function to plot a heatmap of log-fold changes for selected samples and genes
plot_logfold_change_heatmap <- function(filtered_DEP, gene_list, contrast_list, colors = c("blue", "white", "red"), remove_na_genes = FALSE) {
  # Check if all contrasts in contrast_list exist in filtered_DEP
  missing_contrasts <- setdiff(contrast_list, names(filtered_DEP))
  if (length(missing_contrasts) > 0) {
    stop(paste("The following contrasts are not found in filtered_DEP:", paste(missing_contrasts, collapse = ", ")))
  }

  # Filter data for the selected genes and contrasts
  filtered_data <- filtered_DEP[contrast_list] %>%
    lapply(function(df) {
      df <- as.data.frame(df)  # Convert to a standard data frame
      df %>% 
        filter(Symbol %in% gene_list) %>% 
        dplyr::select(Symbol, log2_ratio)  # Use dplyr::select to avoid conflicts
    })

  # Combine all filtered data into one data frame
  combined_data <- bind_rows(filtered_data, .id = "Contrast")

  # Pivot data to wide format for easier manipulation
  data_wide <- combined_data %>%
    pivot_wider(names_from = Contrast, values_from = log2_ratio) %>%
    as.data.frame()  # Ensure it is a standard data frame

  # If remove_na_genes is TRUE, filter out rows where all log2FC values are NA
  if (remove_na_genes) {
    data_wide <- data_wide %>%
      filter(rowSums(is.na(dplyr::select(., -Symbol))) < (ncol(data_wide) - 1))
  }

  # Pivot data back to long format for ggplot
  data_long <- data_wide %>%
    pivot_longer(cols = -Symbol, names_to = "Contrast", values_to = "Log2FC")

  # Calculate the maximum absolute value of Log2FC to set symmetric color limits
  max_abs_log2fc <- max(abs(data_long$Log2FC), na.rm = TRUE)

  # Create the heatmap using ggplot
  heatmap_plot <- ggplot(data_long, aes(x = Contrast, y = Symbol, fill = Log2FC)) +
    geom_tile(color = "white") +
    scale_fill_gradient2(
      low = colors[1], mid = colors[2], high = colors[3], 
      midpoint = 0, limits = c(-max_abs_log2fc, max_abs_log2fc), 
      oob = scales::squish # ensures values outside the range are squished within the scale
    ) +
    theme_minimal() +
    theme(
      axis.text.x = element_text(angle = 90, hjust = 1),
      aspect.ratio = length(unique(data_long$Symbol)) / length(unique(data_long$Contrast))
    ) +
    labs(title = "Log-Fold Change Heatmap", x = "Samples", y = "Genes")

  return(heatmap_plot)
}



# Example usage
# Define your gene list and contrasts of interest
gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO","IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")

# Call the function to create a heatmap
heatmap_plot <- plot_logfold_change_heatmap(df_list, gene_list, contrast_list, remove_na_genes = TRUE)

# Print the heatmap
print(heatmap_plot)


```

Same function but controling keeping the gene list order

```{r}
library(ggplot2)
library(dplyr)
library(tidyr)

# Function to plot a heatmap of log-fold changes for selected samples and genes
plot_logfold_change_heatmap_ordered <- function(filtered_DEP, gene_list, contrast_list, colors = c("blue", "white", "red"), remove_na_genes = FALSE) {
  # Check if all contrasts in contrast_list exist in filtered_DEP
  missing_contrasts <- setdiff(contrast_list, names(filtered_DEP))
  if (length(missing_contrasts) > 0) {
    stop(paste("The following contrasts are not found in filtered_DEP:", paste(missing_contrasts, collapse = ", ")))
  }

  # Filter data for the selected genes and contrasts
  filtered_data <- filtered_DEP[contrast_list] %>%
    lapply(function(df) {
      df <- as.data.frame(df)  # Convert to a standard data frame
      df %>% 
        filter(Symbol %in% gene_list) %>% 
        dplyr::select(Symbol, log2_ratio)  # Use dplyr::select to avoid conflicts
    })

  # Combine all filtered data into one data frame
  combined_data <- bind_rows(filtered_data, .id = "Contrast")

  # Pivot data to wide format for easier manipulation
  data_wide <- combined_data %>%
    pivot_wider(names_from = Contrast, values_from = log2_ratio) %>%
    as.data.frame()  # Ensure it is a standard data frame

  # If remove_na_genes is TRUE, filter out rows where all log2FC values are NA
  if (remove_na_genes) {
    data_wide <- data_wide %>%
      filter(rowSums(is.na(dplyr::select(., -Symbol))) < (ncol(data_wide) - 1))
  }

  # Pivot data back to long format for ggplot
  data_long <- data_wide %>%
    pivot_longer(cols = -Symbol, names_to = "Contrast", values_to = "Log2FC")

  # Set factor levels for genes and contrasts to maintain the specified order
  data_long$Symbol <- factor(data_long$Symbol, levels = gene_list)
  data_long$Contrast <- factor(data_long$Contrast, levels = contrast_list)

  # Calculate the maximum absolute value of Log2FC to set symmetric color limits
  max_abs_log2fc <- max(abs(data_long$Log2FC), na.rm = TRUE)

  # Create the heatmap using ggplot
  heatmap_plot <- ggplot(data_long, aes(x = Contrast, y = Symbol, fill = Log2FC)) +
    geom_tile(color = "white") +
    scale_fill_gradient2(
      low = colors[1], mid = colors[2], high = colors[3], 
      midpoint = 0, limits = c(-max_abs_log2fc, max_abs_log2fc), 
      oob = scales::squish # ensures values outside the range are squished within the scale
    ) +
    theme_minimal() +
    theme(
      axis.text.x = element_text(angle = 45, hjust = 1),
      aspect.ratio = length(unique(data_long$Symbol)) / length(unique(data_long$Contrast))
    ) +
    labs(title = "Log-Fold Change Heatmap (Ordered)", x = "Samples", y = "Genes")

  return(heatmap_plot)
}

# Example usage
# Define your gene list and contrasts of interest
gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")

# Call the function to create a heatmap
heatmap_plot_ordered <- plot_logfold_change_heatmap_ordered(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE)

# Print the heatmap
print(heatmap_plot_ordered)

```

Plot by dendrogram

```{r}
plot_logfold_change_heatmap_dendrogram <- function(df_list, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 0.8) {
  # Filter the dataframes to include only the selected genes and contrasts
  filtered_data <- df_list[contrast_list] %>% 
    lapply(function(df) {
      df %>% 
        dplyr::filter(Symbol %in% gene_list) %>% 
        dplyr::select(Symbol, log2_ratio)
    }) %>% 
    dplyr::bind_rows(.id = "Contrast")
  
  # Convert to a wide format for the heatmap
  data_wide <- filtered_data %>%
    tidyr::pivot_wider(names_from = Contrast, values_from = log2_ratio)
  
  # Optionally remove genes with all NA values across contrasts
  if (remove_na_genes) {
    data_wide <- data_wide %>%
      dplyr::filter(rowSums(is.na(dplyr::select(., -Symbol))) < ncol(data_wide) - 1)
  }
  
  # Convert Symbol to row names for the heatmap
  data_wide <- data_wide %>%
    tibble::column_to_rownames("Symbol")
  
  # Create a matrix for the heatmap
  mat <- as.matrix(data_wide)
  
  # Determine the limits for the scale to be symmetric around zero
  max_val <- max(abs(mat), na.rm = TRUE)
  
  # Plot the heatmap with dendrogram ordering
  pheatmap::pheatmap(
    mat,
    cluster_rows = TRUE,
    cluster_cols = TRUE,
    color = colorRampPalette(c("blue", "white", "red"))(100),
    breaks = seq(-max_val, max_val, length.out = 101),
    cellwidth = column_width,
    cellheight = column_width,
    na_col = "grey",
    scale = "none" # or you can use "row" or "column" if scaling is needed
  )
}


p <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 10)
print(p)



```

```{r, fig.height=5}


# now plotted with function below

gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1)
print(p1)


gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
p2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1)
print(p2)

gene_list <- lysome$`Gene name`
contrast_list <- c("IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p3 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1)
print(p3)


gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1)
print(p4)


```


```{r}


gene_list <- lysome$`Gene name`
contrast_list1 <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
contrast_list2 <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
contrast_list3 <- c("IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
contrast_list4 <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")



pdf("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/HM_lysomeList_Log2FC_dendrogram.pdf", width = 6, height = 8)
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list1, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Lysosome Genes")
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list1, remove_na_genes = TRUE, column_width = 1,title = "Log Fold Change Lysosome Genes")
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list3, remove_na_genes = TRUE, column_width = 1,title = "Log Fold Change Lysosome Genes")
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list4, remove_na_genes = TRUE, column_width = 1,title = "Log Fold Change Lysosome Genes")
dev.off()


```

get another gene list from Roxanne's list


```{r}
mitocarta <- read_excel("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/List of genes for RNAseq or Proteomics/MitoCarta3.0.xlsx")

mito.genes <- mitocarta$Symbol
length(mito.genes)


```
```{r}

gene_list <- mito.genes[1:20]
#gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 12)
print(p1)


contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
p2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 12)
print(p2)

contrast_list <- c("IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p3 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 12)
print(p3)

contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 12)
print(p4)

```

New function to skip genes not found in df_list

```{r}

plot_logfold_change_heatmap_dendrogram <- function(df_list, gene_list, contrast_list, 
                                                   remove_na_genes = FALSE, column_width = 0.8) {
  # Filter data to only include the specified genes and contrasts
  filtered_data <- df_list[contrast_list] %>%
    lapply(function(df) {
      df %>% 
        dplyr::filter(Symbol %in% gene_list) %>% 
        dplyr::select(Symbol, log2_ratio)
    }) %>% 
    dplyr::bind_rows(.id = "Contrast")
  
  # Ensure only genes in gene_list that are found in the filtered data are kept
  filtered_data <- filtered_data %>%
    dplyr::filter(Symbol %in% unique(filtered_data$Symbol))
  
  # Create a wide format matrix for heatmap plotting
  data_wide <- filtered_data %>%
    tidyr::pivot_wider(names_from = Contrast, values_from = log2_ratio) %>%
    dplyr::filter(Symbol %in% gene_list) %>% # Ensure only genes in gene_list are kept
    dplyr::mutate(across(-Symbol, as.numeric))

  # Optionally remove genes that are NA across all contrasts
  if (remove_na_genes) {
    data_wide <- data_wide %>%
      dplyr::filter(rowSums(is.na(dplyr::select(., -Symbol))) < ncol(data_wide) - 1)
  }

  # Create a matrix for heatmap plotting
  mat <- as.matrix(data_wide %>% dplyr::select(-Symbol))
  rownames(mat) <- data_wide$Symbol
  
  # Create the heatmap with dendrograms
  pheatmap::pheatmap(mat, 
                     cluster_rows = TRUE, 
                     cluster_cols = TRUE, 
                     scale = "none", 
                     color = colorRampPalette(c("blue", "white", "red"))(50),
                     cellwidth = column_width * 10, # Adjust column width
                     show_rownames = TRUE,
                     show_colnames = TRUE)
}

# Example usage



gene_list <- mitocarta$Symbol[11:20]
print(gene_list)

contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = c("PINK1","GFAP","S100B","SNCA","VMAT2","GLUT2","PRKN"), contrast_list, 
                                       remove_na_genes = TRUE, column_width = 1)

plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol[1:10], contrast_list, 
                                       remove_na_genes = TRUE, column_width = 1)


plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol[11:20], contrast_list, 
                                       remove_na_genes = TRUE, column_width = 1)

print(mitocarta$Symbol[1:11])
print(mitocarta$Symbol[11:20])

```

This error seems to mean there are duplicate enteries for the same gene

```{r}

plot_logfold_change_heatmap_dendrogram <- function(df_list, gene_list, contrast_list, 
                                                   remove_na_genes = FALSE, column_width = 0.8) {
  # Filter data to only include the specified genes and contrasts
  filtered_data <- df_list[contrast_list] %>%
    lapply(function(df) {
      df %>% 
        dplyr::filter(Symbol %in% gene_list) %>% 
        dplyr::select(Symbol, log2_ratio)
    }) %>% 
    dplyr::bind_rows(.id = "Contrast")
  
  # Ensure only genes in gene_list that are found in the filtered data are kept
  filtered_data <- filtered_data %>%
    dplyr::filter(Symbol %in% unique(filtered_data$Symbol))
  
  # Handle duplicates by summarizing (e.g., taking the mean)
  filtered_data <- filtered_data %>%
    dplyr::group_by(Symbol, Contrast) %>%
    dplyr::summarize(log2_ratio = mean(log2_ratio, na.rm = TRUE), .groups = 'drop')
  
  # Create a wide format matrix for heatmap plotting
  data_wide <- filtered_data %>%
    tidyr::pivot_wider(names_from = Contrast, values_from = log2_ratio) %>%
    dplyr::filter(Symbol %in% gene_list) %>% # Ensure only genes in gene_list are kept
    dplyr::mutate(dplyr::across(-Symbol, as.numeric))

  # Optionally remove genes that are NA across all contrasts
  if (remove_na_genes) {
    data_wide <- data_wide %>%
      dplyr::filter(rowSums(is.na(dplyr::select(., -Symbol))) < ncol(data_wide) - 1)
  }

  # Create a matrix for heatmap plotting
  mat <- as.matrix(data_wide %>% dplyr::select(-Symbol))
  rownames(mat) <- data_wide$Symbol
  
  # Create the heatmap with dendrograms
  pheatmap::pheatmap(mat, 
                     cluster_rows = TRUE, 
                     cluster_cols = TRUE, 
                     scale = "none", 
                     color = colorRampPalette(c("blue", "white", "red"))(50),
                     cellwidth = column_width * 10, # Adjust column width
                     show_rownames = TRUE,
                     show_colnames = TRUE)
}


plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol[1:10], contrast_list, 
                                       remove_na_genes = TRUE, column_width = 1)


plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol[10:20], contrast_list, 
                                       remove_na_genes = TRUE, column_width = 1)



plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol[11:60], contrast_list, 
                                       remove_na_genes = TRUE, column_width = 0.8)


plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol[1:20], contrast_list, 
                                       remove_na_genes = TRUE, column_width = 1)


```
We now need to remove problematic values 

```{r}
plot_logfold_change_heatmap_dendrogram <- function(df_list, gene_list, contrast_list, 
                                                   remove_na_genes = FALSE, column_width = 0.8) {
  # Filter data to only include the specified genes and contrasts
  filtered_data <- df_list[contrast_list] %>%
    lapply(function(df) {
      df %>% 
        dplyr::filter(Symbol %in% gene_list) %>% 
        dplyr::select(Symbol, log2_ratio)
    }) %>% 
    dplyr::bind_rows(.id = "Contrast")
  
  # Ensure only genes in gene_list that are found in the filtered data are kept
  filtered_data <- filtered_data %>%
    dplyr::filter(Symbol %in% unique(filtered_data$Symbol))
  
  # Handle duplicates by summarizing (e.g., taking the mean)
  filtered_data <- filtered_data %>%
    dplyr::group_by(Symbol, Contrast) %>%
    dplyr::summarize(log2_ratio = mean(log2_ratio, na.rm = TRUE), .groups = 'drop')
  
  # Create a wide format matrix for heatmap plotting
  data_wide <- filtered_data %>%
    tidyr::pivot_wider(names_from = Contrast, values_from = log2_ratio) %>%
    dplyr::filter(Symbol %in% gene_list) %>% # Ensure only genes in gene_list are kept
    dplyr::mutate(dplyr::across(-Symbol, as.numeric))

  # Optionally remove genes that are NA across all contrasts
  if (remove_na_genes) {
    data_wide <- data_wide %>%
      dplyr::filter(rowSums(is.na(dplyr::select(., -Symbol))) < ncol(data_wide) - 1)
  }

  # Create a matrix for heatmap plotting
  mat <- as.matrix(data_wide %>% dplyr::select(-Symbol))
  rownames(mat) <- data_wide$Symbol
  
  # Check for NA, NaN, or Inf values in the matrix and remove any rows or columns that contain them
  mat <- mat[complete.cases(mat), ]  # Remove rows with NA/NaN/Inf values
  mat <- mat[, colSums(is.na(mat)) == 0]  # Remove columns with NA/NaN/Inf values
  
  # If after removing NA rows/columns the matrix becomes empty, return an informative error
  if (nrow(mat) == 0 || ncol(mat) == 0) {
    stop("The matrix is empty after removing rows/columns with NA/NaN/Inf values. No valid data to plot.")
  }
  
  # Create the heatmap with dendrograms
  pheatmap::pheatmap(mat, 
                     cluster_rows = TRUE, 
                     cluster_cols = TRUE, 
                     scale = "none", 
                     color = colorRampPalette(c("blue", "white", "red"))(50),
                     cellwidth = column_width * 10, # Adjust column width
                     show_rownames = TRUE,
                     show_colnames = TRUE)
}

plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol[1:10], contrast_list, 
                                       remove_na_genes = TRUE, column_width = 1)


plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol[10:20], contrast_list, 
                                       remove_na_genes = TRUE, column_width = 1)



plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol[11:60], contrast_list, 
                                       remove_na_genes = TRUE, column_width = 0.8)


plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol[1:20], contrast_list, 
                                       remove_na_genes = TRUE, column_width = 1)

plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol, contrast_list, 
                                       remove_na_genes = TRUE, column_width = 1)


```
Force 0 to be the center of the scale


```{r}
plot_logfold_change_heatmap_dendrogram <- function(df_list, gene_list, contrast_list, 
                                                   remove_na_genes = FALSE, column_width = 0.8, title = "Log Fold Change") {
  # Filter data to only include the specified genes and contrasts
  filtered_data <- df_list[contrast_list] %>%
    lapply(function(df) {
      df %>% 
        dplyr::filter(Symbol %in% gene_list) %>% 
        dplyr::select(Symbol, log2_ratio)
    }) %>% 
    dplyr::bind_rows(.id = "Contrast")
  
  # Ensure only genes in gene_list that are found in the filtered data are kept
  filtered_data <- filtered_data %>%
    dplyr::filter(Symbol %in% unique(filtered_data$Symbol))
  
  # Handle duplicates by summarizing (e.g., taking the mean)
  filtered_data <- filtered_data %>%
    dplyr::group_by(Symbol, Contrast) %>%
    dplyr::summarize(log2_ratio = mean(log2_ratio, na.rm = TRUE), .groups = 'drop')
  
  # Create a wide format matrix for heatmap plotting
  data_wide <- filtered_data %>%
    tidyr::pivot_wider(names_from = Contrast, values_from = log2_ratio) %>%
    dplyr::filter(Symbol %in% gene_list) %>% # Ensure only genes in gene_list are kept
    dplyr::mutate(dplyr::across(-Symbol, as.numeric))

  # Optionally remove genes that are NA across all contrasts
  if (remove_na_genes) {
    data_wide <- data_wide %>%
      dplyr::filter(rowSums(is.na(dplyr::select(., -Symbol))) < ncol(data_wide) - 1)
  }

  # Create a matrix for heatmap plotting
  mat <- as.matrix(data_wide %>% dplyr::select(-Symbol))
  rownames(mat) <- data_wide$Symbol
  
  # Check for NA, NaN, or Inf values in the matrix and remove any rows or columns that contain them
  mat <- mat[complete.cases(mat), ]  # Remove rows with NA/NaN/Inf values
  mat <- mat[, colSums(is.na(mat)) == 0]  # Remove columns with NA/NaN/Inf values
  
  # If after removing NA rows/columns the matrix becomes empty, return an informative error
  if (nrow(mat) == 0 || ncol(mat) == 0) {
    stop("The matrix is empty after removing rows/columns with NA/NaN/Inf values. No valid data to plot.")
  }
  
  # Determine the limits for the scale to be symmetric around zero
  max_val <- max(abs(mat), na.rm = TRUE)
  
  # Create the heatmap with dendrograms
  pheatmap::pheatmap(mat, 
                     cluster_rows = TRUE, 
                     cluster_cols = TRUE, 
                     scale = "none", 
                     color = colorRampPalette(c("blue", "white", "red"))(50),
                     breaks = seq(-max_val, max_val, length.out = 51), # Symmetric color scale
                     cellwidth = column_width * 10, # Adjust column width
                     show_rownames = TRUE,
                     show_colnames = TRUE,
                     main = title)
}

plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol, contrast_list, 
                                       remove_na_genes = TRUE, column_width = 1)



```

Gene different heatmaps

```{r}
gene_list <- mito.genes
#gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes")
print(p1)


contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
p2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes")
print(p2)

contrast_list <- c("IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p3 <-plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes")
print(p3)

contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes")
print(p4)


contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[1:100]
p1.1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 1-100")
p1.1 


contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[101:200]
p1.2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 101-200")
p1.2

contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[201:300]
p1.3 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 201-300")
p1.3

contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[301:400]
p1.4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 301-400")
p1.4

contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[401:600]
p1.5 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 401-600")
p1.5

contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[601:800]
p1.6 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 601-800")
p1.6

contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[801:1136]
p1.7 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 801-1136")
p1.7



```

Save mitocharta plots

```{r}
pdf("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/HM_Mitocharta_Log2FC_dendrogram.pdf")
p1
p1.1
p1.2
p1.3
p1.4
p1.5
p1.6
p1.7
p2
p3
p4
dev.off()

```

Read in another list of genes

```{r}

pd.genes.list <- read_excel("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/List of genes for RNAseq or Proteomics/PD gene list.xlsx", skip = 1)
head(pd.genes.list)
colnames(pd.genes.list) <- c("Symbol","Accession","Description","Ref","Species")
head(pd.genes.list)
pd.genes <- pd.genes.list$Symbol

```
Look at the gene list for PD

```{r}

gene_list <- pd.genes
#gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes")
print(p1)


contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
p2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes")
print(p2)

contrast_list <- c("IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p3 <-plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes")
print(p3)

contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes")
print(p4)


contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[1:50]
p1.1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 1-50")
p1.1 


contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[50:100]
p1.2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 50-100")
p1.2

contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[100:150]
p1.3 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 100-150")
p1.3

contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[150:200]
p1.4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 150-200")
p1.4

contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[200:250]
p1.5 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 200-250")
p1.5

contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[250:300]
p1.6 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 250-300")
p1.6

contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[300:330]
p1.7 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 300-330")
p1.7


```

```{r}
pdf("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/HM_PD_genes_Log2FC_dendrogram.pdf")
p1
p1.1
p1.2
p1.3
p1.4
p1.5
p1.6
p1.7
p2
p3
p4
dev.off()

```

Read synapse list 

```{r}
synapse.genes.list <- read_excel("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/List of genes for RNAseq or Proteomics/SYNAPSE_REDUCED_GENE_LIST.xlsx", col_names = FALSE)
head(synapse.genes.list)
colnames(synapse.genes.list) <- c("Symbol")
head(synapse.genes.list)
synapse.genes <- synapse.genes.list$Symbol
```

```{r}
gene_list <- synapse.genes
#gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes")
print(p1)


contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
p2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes")
print(p2)

contrast_list <- c("IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p3 <-plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes")
print(p3)

contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes")
print(p4)


contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- synapse.genes[1:100]
p1.1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes 1-100")
p1.1 


contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- synapse.genes[100:200]
p1.2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes 100-200")
p1.2

contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- synapse.genes[200:300]
p1.3 <-plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes 200-300")
p1.3

contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- synapse.genes[300:400]
p1.4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes 300-400")
p1.4

contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- synapse.genes[400:500]
p1.5 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes 400-500")
p1.5

contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- synapse.genes[500:594]
p1.6 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes 500-594")
p1.6

```

save the synaptic gene list

```{r}
pdf("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/HM_Synpatic_genes_Log2FC_dendrogram.pdf")
p1
p1.1
p1.2
p1.3
p1.4
p1.5
p1.6
p2
p3
p4
dev.off()
```


```{r}

brainD.genes.list <- read.csv("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/List of genes for RNAseq or Proteomics/Brain Diseases - BrainBase - CNCB-NGDC.csv")
head(brainD.genes.list)
# cannot use - gene.symbol needs would need to be converted to Symbol and the list is long so it wouldn't be very useful
# try with all and then filter differently




```
GWAS PD

```{r}
gwas.genes <- read.csv("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/List of genes for RNAseq or Proteomics/ALL_PD_GWAS_GENELIST.csv")
head(gwas.genes)


```
All gwas genes


```{r}
gene_list <- gwas.genes$hgnc_symbol
#gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change All GWAS Genes")
print(p1)


contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
p2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change All GWAS Genes")
print(p2)

contrast_list <- c("IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p3 <-plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change All GWAS Genes")
print(p3)

contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p4 <- p4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change All GWAS Genes")
print(p4)


contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")

gene_list <- gwas.genes %>%
  filter(distance_to_closest_snp == 0) %>%
  pull(hgnc_symbol)
print(gene_list)
  
p1.1 <- p4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change GWAS Genes with distance = 0")
p1.1 


gene_list <- gwas.genes %>%
  filter(distance_to_closest_snp < 10000 & distance_to_closest_snp > 0) %>%
  pull(hgnc_symbol)
print(gene_list)


print(gene_list)
p1.2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change GWAS Genes with distance > 0 and < 10000")
p1.2

gene_list <- gwas.genes %>%
  filter(distance_to_closest_snp > 10000) %>%
  pull(hgnc_symbol)
print(gene_list)

p1.3 <-plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change GWAS Genes with distance > 10000")
p1.3




gene_list <- gwas.genes %>%
  filter(distance_to_closest_snp < 10000) %>%
  pull(hgnc_symbol)
print(gene_list)


print(gene_list)
p1.4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = FALSE, column_width = 1, title = "Log Fold Change GWAS Genes with distance < 10000")
p1.4


```
```{r}
pdf("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/HM_GWAS_genes_Log2FC_dendrogram.pdf")
p1
p1.1
p1.2
p1.3
p1.4
p2
p3
p4
dev.off()



```




